As the use of facial recognition technology has become increasingly prevalent, concerns about its potential for misuse have also grown. At Herta, we recognize the importance of responsible use of this technology and have made it our mission to always implement ethical guidelines that prioritize privacy, security, and fairness.

In recent years, facial recognition has been a focus of attention in different sectors due to the laws in some countries. But, beyond the legality that we talked about in our last blog post, we also believe in the importance of adhering to certain ethical principles that ensure a good use of artificial intelligence. We think that these principles, based on proportionality and ethics, must be closely related to our culture. Therefore, that all the people working in Herta attain with them. 

In this blog we will delve in the 7 ethical standards of Herta:

1. Facial recognition without demographic bias. In other words, Herta uses a carefully selected and refined training dataset (more than 50 million images) especially focused on collecting images from underrepresented groups. In addition, it uses a number of other methods: 
   1. Usage balancing to indicate to the network which groups require special consideration. 
   2. Simultaneous training with multiple targets. 
   3. Gaining knowledge about specific groups within the identification task.
   4. Use layers that have demonstrated better generalization.
2. Do not process biometric data when it is not needed. In cases where facial identification is not necessary, the system will only handle the images without cross-referencing the biometric data with the database. In other words, the system will not identify subject, even if they are registered in the database, if it is not necessary.
3. Face blurring or masking. All faces detected by the system and which do not correspond to persons registered in the database will be blurred or hidden in real time. This function of the software allows a great solution within the European legality. Because in the process of anonymisation of the registered photographs, we support and defend the data protection laws (article 26 GDPR).
4. Deletion of detected or identified images. The user can choose a time range for the retention of faces detected or identified by the system.
5. Encryption. The communication of the system (from the beginning with the camera, the edge station, the server as well as the database) is encrypted.
6. Centralisation of the database on the enrolled subjects. Provide security in the processing, such as integrity and confidentiality.
7. Limited access to data on external devices. Mobile phones, tablets, electronic agendas, etc. These will be connected to the system and will receive alerts, but will not be able to download any data from the database.

As we have seen, all these ethical rules contribute to the security and protection of users’ data and Herta is highly committed to contribute to an ethical and respectful deployment of this technology. In this sense, benefits are established in both directions: On one hand, it helps to establish a safety net at the places where the software is installed and, at the same time, it protects the rights of the people on site.

We believe that the responsible use of facial recognition technology is crucial for building trust with our customers and the broader public, and we are committed to setting a positive example in the industry. We look forward to sharing our insights and engaging in discussions around the ethics of facial recognition technology.

Written by: José Torija, Abril Pérez and Laura Blanc

Video surveillance is common in stores and shopping centers to offer greater security to their customers and to have evidence of incidents that may occur. But artificial intelligence, in full bloom and with increasing applications in our daily lives, allows us to obtain much more from a simple security camera.

Herta, a company specializing in facial recognition for security, access control and retail; and Neural Labs, a company focused on vehicle access control, cities and intelligent transportation systems and logistics, combine their latest generation innovations to offer benefits that mark a before and after in the management of shopping centers and the retail sector.

What does the joint use of Neural Labs and Herta’s retail solutions offer?

The solutions have two main aspects: security and obtaining data of interest for decision making.

1. Security features

Warnings about trouble makers and their vehicles
By integrating with a VMS and comparing the images with “blacklists”, warnings are generated for the store’s security managers. In addition, it is possible to extract an image of the person leaving or entering their vehicle in order to link them.

Incident resolution in stores
By offering historical searches of people, through forensic analysis of a recorded video in a VMS, and of vehicles according to license plate, color, brand, type of vehicle, time zone.

Search for people of police interest, criminals or missing persons and/or their vehicle
Through the integration with the center’s own or third-party databases, individuals can be located in real time.

On-site tracking of an individual
Through an image of the individual from a previous alarm or a registration in the database, the individual can be located in real time.

Access control for people or vehicles
By integrating solutions to manage barriers or bollards, access control to the facility is improved.

Preventing fraudulent vehicle damage claims
By extracting images of the vehicle as it enters the retailer’s parking lot, it can be demonstrated whether or not damage was caused to the vehicle during its time in the premises.

2. Data collection features

Historical and real time parking statistics on:

– Occupancy: can be differentiated according to zones, days and time intervals.

– Average parking time: can be differentiated according to zones, days and time intervals.

– And more data that can be displayed in an historical or real-time dashboard.

Statistics of visitors within the establishment:

– Counting.

– Occupancy and average dwell time.

– Recurrence.

– And more data accessible on a dashboard accessible online.

Detection of VIPs and their vehicles:
Being able to offer alerts upon their arrival.

Average number of people for each parked vehicle.

Percentage of people arriving by vehicle or arriving on foot.

Statistics of average stay in the car park compared to average stay in the shop:
Very useful information to know if the retailer’s car park is used for any other purposes besides shopping.

Customer knowledge:

– Age and gender.

– Data on their vehicle: (brand, color, type…) and even vehicles without number plates (bicycle, scooter…). These can be useful to detect their economic situation, their tastes, etc.

Customer segmentation with targeted advertising
According to the data mentioned in the previous point.

Detection of entrances with greatest affluence.
Essential information to determine: advertising prices at these points, rental prices of premises in shopping centers or to select the location of certain products according to how much you want to promote their sale.

Detection of establishments with greater or lesser affluence within a shopping center.

Using Herta and Neural Labs solutions together provides great flexibility as they adapt to multiple needs and are also backed by the reliability of these two companies, owners of their own technology and with decades of experience in the sector.

Contact us at info@hertasecurity.com 

When we browse the Internet, visit websites or shop online, we generate information about our interactions on the network that online retailers take advantage of in order to: personalize content, discover new requirements, find out which content/products work best and which should be promoted. There are so many possibilities that digital marketing offers to boost customer engagement. But what would happen if these possibilities could be extrapolated to the offline world?

This is what new retail technology in physical stores is proposing. Thanks to facial recognition, it is possible to generate demographic and behavioral information about the client in physical spaces and in real time. This translates into cost savings and competitive advances because it gives the customer a more customized treatment focused on their needs.

In this blog we will detail which are the 6 most important and outstanding functions: 

1. Information for customer targeting. The software is able to collect information about: age and gender, mood, number of visits made, store tour. On one hand, this analysis can help to make adjustments in areas where price, advertisements or customer service are not the right ones. As well as, hunting trends (where consumers buy more impulsively or where large groups buy small products as gifts). On the other hand, this data will allow the creation of targets and the generation of personalized offers. For example, different ads can be made to rotate depending on the customer in front of them, as well as, it can be used in digital kiosks to provide consumers with personalized offers.

2. Recognition of customers as soon as they enter the store. This feature has two important benefits. First, the software stores the number of visits, sales and preferences of the customers, which can be useful to perceive which products they have seen but not purchased. Secondly, it allows the best service to VIP customers. For example, by creating alerts for employees in which they are provided with the customer’s name and preferences in order to make recommendations more in line with their own tastes.

3. Occupancy control and people counting. It is possible to control the occupancy in real time and its duration of permanence. This can help in terms of security and service, as it will be possible to know which are the peaks with greater affluence and therefore, generate an appropriate coverage of employees and product placement.

4. Identification of criminals. The facial recognition system could be used to establish a database of criminals, detecting and alerting them as soon as they enter the store. In addition, it could provide security agents their identity and location in real time.
5. Identification of employees. Using facial recognition, it would be possible to know precisely when the employee arrives at the store, when he/she is on a break, at what time he/she leaves work… Being a much more accurate way and with less administrative volume than conventional means. Also contributing to increased employee productivity.

6. Point-of-sale verification. Using facial recognition, identities can be verified quickly and payments can be authorized. It would not even require the use of credit cards or smartphones. Therefore, we will ensure that no fraudulent transactions can take place, as this technology ensures that the recognised face is a live face, i.e. it is not a video or a photograph in front of the camera.

On the other hand, there are some important points to bear in mind when using this technology. Firstly, if you want to use it in Europe, you have to comply with the European data protection law. In the following link you will find a blog dedicated to this point. Secondly, in case of the need to identify shoplifters, the use of facial recognition technology must be announced before customers enter the shop so that they give their approval for its use. And finally, it is very important that the company in charge of the software be responsible for regularly deleting the data if it is no longer needed.

As we have seen, the benefits of facial recognition in retail are huge, both for retailers and visitors. As a multi-purpose tool, we can increase the security of an establishment while collecting the data needed to increase customer satisfaction. Definitely the technology of the future in retail.

If you want to know how it is used in reality, we offer you with 5 current use cases in this link.

Written by: Abril Pérez | Marketing Trainee

Face recognition has been one of the tools that has helped the most when securing society, it’s been used for detecting criminals in crowded environments, capturing the temperature of sick people, helping in the analysis of microexpressions in forensic evidence…

In order to help understand the right use of this technology, the European Council has elaborated on a series of legal standards. In this blog we will go through some of them.  

To understand the legality of the use of facial recognition we have to go back to the four fundamental rights adopted by the European Council and Parliament. These are neither absolute nor unlimited, but must be in balance with each other.

• The Right to the protection of personal data
• The Right to liberty and security
• The rights to life and to health
• The right to an effective remedy and to a fair trial

But, what happens when different rights conflict with each other? For example, in an airport it is more important to protect the freedom and security of travelers or even their rights to life and health in situations of terrorist or health attacks. Likewise, the forensic use of facial recognition can provide important evidence in a trial. 

In these scenarios, which of them will be the one that will prevail?

We can find the answer on article 9.2 of GDPR. It identifies the cases in which facial recognition can be used:

Substancial public interest:

In other words, from a public security point of view, this kind of system must be available at least for the most threatened infrastructures and facilities. When we talk about threatened infrastructures, we refer to: 

• All buildings related to strategic industries or supplying: energy, chemicals, water…
• Buildings with cultural value
• Places where a lot of people can gather: stadiums, shopping centers, concert halls…

Reasons of public interest in the area of public health:

In order to fight against “serious cross-border threats to health”. For example, in pandemic situations such as COVID-19, for searching people who weren’t wearing a mask or analyzing their body temperature.

Establishment, exercise or defense of legal claims:

When the processing of biometric data (such as face recognition) can be used to prepare or defend against any kind of legal claims.

In broadterms, we could say that face recognition is justified in situations where the most fundamental rights must be protected: security, health and justice. 

Written by: José Torija | Legal Counsel

How can I use facial recognition in sport stadiums?

Sports stadiums are often subject to vandalism and, as places where large numbers of people congregate, any incident can have serious consequences for the safety of those attending.

Prevent access of non granted people

Conventional security systems in charge of detecting altercations cannot limit the access of specific people to the stadiums due to the fact that the entrances are not usually nominative. Ensuring a safe entrance to the stadium is critical, as it’s a very sensitive area where many people gather together while waiting for doors to open.

To prevent blacklisted people from entering, stadiums or events install access control systems that scan all faces as they enter, even recognizing them when wearing a face mask and making sure they are wearing it, while automatically denying access to previous offenders.

In this way, only those who break the law will have this recognition, since the rest of the attendees who have not committed any infringement before, will remain anonymous to the software.

Higher security during the event

In the event that facial analysis is not used at the entrances, this technology allows scanning a stand or a crowd and instantly detecting people on a blacklist. In this way, in combination with a good quality of video surveillance cameras, it is possible to analyze all the faces in real-time and send an alert if the presence of any person whose entry to the premises is not authorized, or whose behavior is unwanted, is detected.

In combination with a VMS software, it’s possible to analyze multiple video images after the event at speeds much faster than real-time, enabling a fast and smooth forensic analysis.

Nominative accreditations

Increasingly, event organizers are turning to facial registration techniques to speed up access control at events. This process is not only extremely simple but also allows, at the same time, quick authentication while ensuring greater security in the area where the event is going to take place.

In that sense, the registration process would be the same as always, starting by including the personal data of the person and adding only an image similar to the one of the passport or identification card should be added. In this way, at the time of entry, only one camera would be needed that would allow face-by-face analysis in a non-invasive and fast way.

Attendee statistics

Another application in this industry is crowd scanning for demographic data so that ads can be properly targeted to the demographic that is present on that specific day. This data is fully anonymized to provide organizations with valuable information while protecting the privacy of fans and attendees.

On the other hand, you can also have very complete information on the quality of assistance, such as: how many attendees there have been, average dwell time, recurrence, occupancy control, or even which accesses had a greater influx of people.

The benefits that facial recognition technology can bring to the sports and events sector is clear, and this is why many soccer fields and large event organizations already have Herta’s technology to guarantee a better experience for attendees and, at the same, time gain a greater knowledge of your public to optimize your sales strategies.

If this is of your interest, check out our different solutions to learn more about the multiple applications facial recognition software has to offer, or book your free demo and one of our representatives will get back to you almost as fast as our software!

Written by: Laura Blanc | CMO, Herta

If you are considering installing a facial recognition software in your business or infrastructure, you will surely feel overwhelmed by the number of providers that are now on the market.

But you may also be wondering what the key factors are to discern between a good or a bad system, since finding the one that meets your needs and is reliable at the same time, is not an easy task.

Regardless of whether you work through an integrator or not, here are 5 tips that will help you differentiate between a good or bad facial recognition system:

1) ACCURACY

Yes, they all claim to be the most accurate on the market. There are going to be some that even dare to provide matching rates of 99% or 100%.

An advice? Do not believe that. Such percentages are very difficult to be achieved in real environments, since the conditions are different from those of a test environment in which external factors are controllable.

With external factors we mean: lightning conditions, the quality of the images in the database or the IP camera position,

So, in the real world, precision is something that will be achieved by having the facial recognition software working in situ. It is very important to be able to get a demo before purchasing any product, and make sure it is a system that has a good reputation among its customers and with existing references.

2) SPEED

Although in a video surveillance installation with facial recognition one of the key factors is the level of reliability of the system (that is, a low percentage of false positives), what is also very important is that it is accompanied by a high speed in said system. result.

The reason is clear: to be able to detect and act in time. For this, the ideal is that the system can offer identifications in real time.

3) ADAPTABILITY

Another indication that the facial identification system is of good quality is that it can be adapted to the existing infrastructure. So if you have cameras installed along with a VMS to monitor them, the software should be able to adapt and integrate seamlessly.

In addition, it should also be possible to scale the system to other locations and be able to centralize everything in one place.

4) PRIVACY PROTECTION

We will all agree that security is more important than privacy, which is why you will be resorting to increasing the security of your business with facial recognition.

But it is also very important to protect the privacy of other people, so you must ensure that the software does not violate anyone’s right to privacy. This will depend on the legislation of the country you are in, so the company should be able to offer encrypted databases or image deletion from time to time.

Another factor to take into account is the manufacturing origin of the products. It is important to check that the product you are buying does not have a use ban in your country. If you live in the UE, this article will help.

5) FINAL COST

Today many companies minimize the necessary use of hardware, even going so far as to use edge systems to adapt to the client’s budget without affecting the performance of the software. Unlike CPU-based systems, software that uses GPUs to process video streams allows you to analyze more cameras on the same GPU.

Even if you have ensured that these main points are guaranteed, it is important to always test before purchasing. Optimal demo licenses should last up to 2 months, that’s the enough amount of time for a company and its IT managers to “play” with the software and see if it works well with their conditions.

Written by: Laura Blanc | CMO, Herta

Video analytics are more than just security, they can now provide valuable information to retailers.

It is true and commonly thought that the video cameras installed in retail stores are mainly used for security purposes, as to prevent theft and detect habitual shoplifting incidents in real-time. 

But now it’s possible to use the existing installed infrastructure for more than security purposes only. A powerful “two-in-one” tool aimed to tackle the driving factor of the need to analyze and manage data and metrics, in addition to security.

How do video analytics work?

Video Analytics is a technology used to analyze a video for specific data, behavior, or attitudes. The software algorithms are performed on processors within a computer or platform embedded in video cameras, recording devices, or specialized video processing units. 

One of the most common uses is for real-time and post-event video analysis from camera video streams. In this case, the algorithms analyze frame by frame in order to detect abnormal movements, certain objects or different image changes. 

The analytic capabilities offered today are limited only by the creativity of the user and the need presented.

What information do retail video analytics provide?

We all know that what retailers value the most is data and real information. Common retail video analytics are used for loss prevention, inventory management or queue detection. 

Smart video analytics nowadays offer highly valuable information in real-time that allow store managers to react on time, make the right decisions at the right moment and provide them with critical information to determine their business strategy.

In this sense, retailers can benefit from the following information obtained from anonymous face video analytics:

• People counting: Know the number of visitors in your store in real-time
• Visitor profiling: See how many adults, children, men or women visit your store
• Dwell time: Have knowledge of the average time spent by customers inside your store
• Customer recurrence:  Understand how many times per week, month or year a customer visits your store
• Occupancy: See the level of occupancy of your business to ensure a healthy environment
• Conversion rate: Know how many visitors enter your store and leave with a purchase
• Hot areas: Similar to heat mapping, anonymous face analytics let you gather information of which areas are the most visited or which entrances and exits have greater influx of people

This type of data from retail analytics software is the best ally to improve the buying and selling experience in the retail business. 

Additionally, anonymous face analytics help to improve a store marketing campaigns by providing real-time targeted advertising. Based on demographic information such as age or gender, the advertising displayed varies and adjusts to general group preferences or to show certain offers on specific periods of time (ie. Christmas Sales, Black Friday, Mother’s Day…).

Knowing your customers and how they behave in your store with information collected in an online dashboard, allows the user to have live information which helps reach the optimal store performance. 

What are facial expressions and how does your analysis work?

The automatic analysis of facial expressions is motivated by the essential role that the face plays in our emotional and social life. Facial expression is one of the most convincing and natural means that human beings have to communicate our emotions, intentions, clarify and emphasize what we say. Furthermore, unlike other non-verbal channels, facial expressions are cross-cultural and universal, not depending on the age and gender of the individual.

In the context of an interview or interrogation, the analysis of facial expressions can provide invaluable support to the observer. The spotter can assess, for example, in what moments they occur in relation to the question posed: when listening to it, while processing that information; when answering, after having given the answer. It is also interesting for the detection of emotional incongruities, that is, situations in which the subject verbally expresses an emotion by showing a very different one on the face. Likewise, the direction of the gaze and the orientation of the head over time translate the degree of attention of the interviewee, giving clues about their interest, abilities and certain personality traits.

How does facial expression analysis work?

The system analyzes the face frame by frame, either from a pre-recorded video or from a camera capture in real time.

• It begins by detecting the presence and location of the face within the frame.
• Next, it extracts a series of characteristic points of the face (for example, around the eyes, eyebrows, nose and mouth).
• Finally, the basic emotions, micro-expressions and behavioral metrics are extracted.

The classification algorithms are based on Deep Learning, an advanced Artificial Intelligence technique that uses deep neural networks. These algorithms are capable of automatically extracting the most relevant information from the face, such as patterns and textures (for example, presence of wrinkles around the eyes, shape of the mouth, etc.). Our system has been trained with an extensive database of millions of images of subjects of different ages, genders and ethnicities. This allows BioObserver to ensure a robust and universal behavior, with very high hit rates.

THE BASIC EMOTIONS

The field of Psychology considers that human beings have a reduced number of basic emotions, from which our entire affective range is built. These emotions are innate, and their corresponding facial expressions are universally recognized.

The 7 most used categories of basic emotions are those proposed by the psychologist Paul Ekman: “joy”, “sadness”, “fear”, “anger”, “aversion”, “surprise” and “neutral”.

THE FACIAL MICRO-EXPRESSIONS

The human face contains more than 43 muscles. Facial micro-expressions are the result of the activation of one or more of them. These are involuntary gestures, which last a twentieth of a second, and can reveal the state of mind that we want to hide. They are reactions that do not go unnoticed by a well-trained eye, but are almost imperceptible to non-expert observers. Paul Ekman’s Facial Action Coding System (FACS) lists all the micro-expressions or Action Units (AUs) that can occur on the face.

BioObserver allows the user to define a custom list of events that are considered of interest (for example, the start / end of a key question), and annotate them in real time on the video. Said annotations can be viewed and exported to a file together with the rest of the information extracted (micro-expressions, basic emotions, valence / activation, gaze / head orientation). In this way, the tool allows establishing relationships between events and the individual’s behavior.

The arrival of Covid-19 has created a variety of challenges for different communities and businesses. In businesses, safety is of significant concern for stakeholders as it is not easy to prevent criminal activities given Covid-19 protocols.

The new normal for post-Covid-19 businesses includes the use of contactless technology standards in order to guarantee employees wellbeing.

One of the latest technologies that can help with security is the Facial Recognition Access Control System. This software can verify a person’s identity even if they are wearing a mask, hence grant their access if this criteria is met.

Let´s discuss how the post Covid-19 world will look and how much impact the facial recognition technology has.

Post Covid-19 World

Although offices, factories, venues, and retail stores have started opening, and activity levels are likely to return to normal soon, signs are indicating that some aspects of this pandemic lifestyle will remain with us for some time.

This is why businesses will need to adopt contactless access control systems as part of their physical security in order to ensure the health and safety of both visitors and staff.

What is Facial Recognition Access Control?

Face Recognition with Access Control is a vital tool due to its contactless nature and high-accuracy index. Before integrating it into your premises, you should first understand its functionality.

This system can be configured to check the face of any person who passes in front of a camera and determine whether the person is wearing a mask or not before letting them enter a certain area. In hospitals, banks, factories, pharmacies, schools, and security areas, this device can be used identify people and grant access only to those with permission.

Face Recognition and Covid-19

For the following reasons, Face Recognition has now become the new normal:

• It can ensure contactless access.
• Facial Recognition software improves identification accuracy.
• Prevents criminal acts or negligence.
• Monitors the attendance of your staff.
• Ensures all visitors and employees wear masks.

What are the major benefits of an access control system?

There are some extremely important benefits of this technology. The advantages of this tool are not limited to its accuracy but can also be found in its power to transform business processes related to security and health protocol compliance.

Let’s discuss some of the major advantages:

• As visitor identity can be verified directly, high security for buildings is achieved.
• Users can self-enroll in the system, saving time.
• The absence or presence of a face mask can be detected.
• Affordable cost of maintenance and installation.
• It can be easily integrated with third-party systems.
• It takes less than 2 hours to have it up and running.

What are the factors you should consider when purchasing?

Let’s get straight into some of the major factors you should take into consideration before acquiring Facial Recognition for Access Control.

1. Sufficient lightening for outdoor use.
   If the desired installation location is outdoors, direct sunlight is a key factor you need to look at. Sunlight can affect the camera and force it to slow down. It would helpful to ask the manufacturer about the ideal lighting conditions for your camera to ensure it will function optimally.
2. Anti-spoofing technology.
   For face recognition systems to work properly, they should differentiate between a photo of person and a real face. This is called anti-spoofing, and when purchasing a Facial Recognition Access Control system, you should know Herta’s latest version of BioAccess includes this feature.

Conclusion

If we look for one major takeaway from this piece, it might be that Facial Recognition for Access Control has proven to be a breath of fresh air for businesses. It not only helps by decreasing the probability of infection but also provides needed security.

If you would like to learn more about the new functionalities above mentioned, do not miss our upcoming webinars where we will dive through them!

We sat down with Ryan Fairclough, Herta’s APAC Sales Manager, to talk ethics within the physical security sector.

How do you see the security sector nowadays?

The Physical Security Industry is a fast-moving technical beast, the availability of new technology coupled with the human urge to feel safety in their environment has led the industry to be one of the fastest growing in the world. Whether it be IP Video Surveillance, Access Control or any other range of products, most people have – knowingly or unknowingly – interacted with a part of this technology on any given day.

How do you expect the security sector will evolve?

Having been involved in this industry for a number of years the evolution has been at a steady but constant pace in all facets of the technology. There were of course early outliers, such as biometric fingerprint access, but they truly lacked any sort of widespread adoption in the wider industry. For the most part, the tech evolution seemed to come more from the management side of the product. Consider the need for a more viable VMS, or [at one time] the never-ending race to launch an IP Video camera with more Megapixels than your competitor, only for real world deployment to be at 1.3MP anyway!

In my opinion, no part of the technology evolution (revolution maybe!) has been as quick and as demanded as the Video Analytics space. It seems overnight that the theory of such concepts moved from people understanding AI to be something out of an Arnold Schwarzenegger film, to those same people demanding its application in their own requirements.

What was once a famous scene in a Tom Cruise movie where our action hero set in some distant future is identified, welcomed and offered products for sale based on his eyes alone is now a reality of sorts. Consider the time it took to move beyond a Coax cable and discussions about TVL lines to the full adoption of Cat5 cables and IP addresses within cameras. The speed of our adoption and demand for Video Analytics seems almost Usain Bolt like in its speed to market.

What about ethics in this sector?

Obviously with this rapid approach to market there has been a range of issues of ethics with the use and application of such technologies. The current world geo-political state and the almost parallel rise of advanced AI brings with it comparisons of George Orwell’s magnificent dystopian novel “1984”. This was even before we entered the now historical year of 2020 where the whole world has been turned on its head by an unseen force unlike anything anyone has ever seen in their own lifetimes, COVID-19.

But are these comparisons fair?

It’s like if you ask me if the use of Analytics Technology, including Facial Recognition Technology is going to send us hurtling towards a point whereby George Orwell’s aforementioned musings [first published in 1949] become some sort of Nostradamus like prophecy?

In my opinion “No” and ultimately my faith in our own industry and humanity at large is at the core of my reasoning.

I heard of some very high-profile cases recently, of alleged misuse of Security technology and one country, rightly or wrongly, comes under pressure regularly for some of their deployments and reasonings. Whilst I am not here to debate the actual reality of these allegations, they do prove that the concept of unethical uses of Surveillance equipment, and in particular smarter AI and Facial Recognition products.

But in noting these allegations and people’s distaste for such uses, most notably ethnic profiling and the “social currency”, I think we reach the realisation that self-monitoring and industry concern will long term outweigh unethical applications.

The simple fact that these questions are being raised at such an embryonic phase of Technology deployment lends credence to the theory that ethical use it at the forefront of the vast majority of our industry.

I do however believe that it is incumbent on us as the Vendors and Integrators of these products to ensure that we are deploying them for the benefit of the future, and not for any nefarious purpose.

How do you deal with this in Australia?

In Australia we have a quant conversational tool called “The Pub Test” to solve basic disagreements. At its simplest, it conveys a situation whereby if you were to find an average person in an average bar and stopped to talk with him, presented all the facts of your application and use case, would that person agree with your use or no? Would they feel comfortable to be part of such a use case?

What would the average person with little to no knowledge of our industry believe to be right in the circumstances?

We have the obligation to ensure we are passing “The Pub Test” and that our applications of Security Technology and Analytics comes from a place of future prosperity for all as opposed to oppression or unsafe purposes. I believe our industry as a whole is more than capable of achieving such a utopian ideology and we can lay to rest the excellent writings of George Orwell as a work of pure Fiction.

As we all know and still feel, COVID-19 has startled many sectors – one of them being the education sector. Today, universities and colleges are seeking for technological innovation to re-open their campuses safely for the already started school year. Undoubtedly this is a significant challenge for everyone working directly at a university or indirectly, especially for the administrators and security teams. It’s of utmost importance to provide students, staff, employees, visiting parents and family members with a hygienic, seamless and preferably touchless, high-assurance identity management starting in physical access control.

Facial recognition is one of the main technological innovations that is helping to fight the spread of COVID-19 in the education sector. It’s being implemented to secure classrooms, building dormitories, athletic facilities, cafeterias and other entrances in several universities. Many other universities have already started evaluating the technology as it offers real advantages when it comes to security and becoming a COVID-19 proof organization. Below a list of the benefits:

Benefits of facial recognition in the education sector:

• Highly accurate and secure, even with masks
• COVID-19 Proof (touchless)
• Affordable (implementation can be finished within a week)
• Hygienic
• Results in great student experience
• Provide traceable accountability
• Flexible and scalable to multiple locations
• No misplaced or stolen badges
• No forgotten passwords
• Integration with existing access control systems is painless

Bonus benefits:

• Existing wiring from a card-reader can be re-used for single or dual-factor authentication
• Simple enrollment of new students, staff or visitors

The benefits that technological innovation can bring is clear, however where do you start when you actually want to deploy quickly without expensive consultants or integrators or run a fast Proof of Concept? How can you be sure that the already made investments in for example hardware or badges can still maintain its usefulness? How can you avoid beginners’ mistakes during a facial recognition project?

We at Herta have been in the business of facial recognition for the past 10 years and have gained experience thanks to our role in more than 200+ high risk and complex facial recognition projects. Furthermore, the know-how of our experienced team in the education sector is profound and references can be shared upon request. Herta technology is most praised for its user experience, speed, accuracy, value for money, ease of integration and its global ecosystem.

Start your evaluation/proof of concept today by getting in touch with me for a Zoom session.

Written by: Tarik Alaca, EMEA Sales Director

Facial Recognition and COVID19

One of the key technologies that will help making the post-COVID19 world a safer place is face recognition. People are usually afraid of being exposed to face recognition systems, thinking they are some kind of malicious Big Brother who watches them. Nothing could be further from the truth. Instead, they are a highly accurate biometric technology that allows -among other applications- touchless and distant access control to workplaces, critical infrastructures, transports or events. With face recognition, there is no need to manipulate access security cards, to approach or put the finger in any device shared by hundreds of persons, thus helping to control pandemic spread. However, there is a main challenge for the face recognition technology in this new normality: the common use of medical masks that occlude half of the face. Ideally, individuals should not have to expose themselves and others to the virus by removing their masks in access controls, but the vast majority of current face recognition algorithms are not yet sufficiently robust to deal with such large facial occlusions.

Indeed, we humans are facing the same challenge. Eye-tracking and psychological works have widely studied the human visual attention mechanisms that take place when confronted to the task of identifying people¹. They demonstrate that we innately and systematically fix the triangular face region formed by the two eyes and the mouth. Therefore, if a vertex of this triangle is occluded by a mask, our long-term acquired face recognition mechanisms will encounter difficulties, lose robustness, and take time to adapt to the new facial configuration. Like humans, face recognition algorithms will also need to adapt themselves to the structure of masked faces.

Deep Learning into action

Existing face recognition algorithms are grounded on Artificial Intelligence (AI), particularly on Machine Learning and Deep Learning techniques. This means that they automatically learn an identification strategy from a dataset of millions of facial images used for their training. Until now, facial training datasets have varied in terms of illumination conditions, head poses or backgrounds; they have also presented certain facial occlusions in the form of eye glasses, caps, scarves or beards. But they hardly ever have contained faces with medical masks!
A possible solution to make face recognition models able to identify persons with masks consists on collecting new training images for that purpose. But there are other, more algorithmic-oriented, alternative approaches. Recently, state-of-the-art academic papers have introduced visual attention mechanisms for Deep Neural Networks, so that they can be taught to focus their attention on specific regions of the image during training². These mechanisms could be applied to teach face recognition models to shift their attention towards the unmasked upper-face region. In any case, face recognition systems must be able to recognize both mask wearers and unmasked people. Therefore, another interesting way to approach the problem could be building a mask detector and then, depending on whether the mask is present or not, and applying different adapted face recognition strategies.
Herta has been working on strong occlusions for the last few months and is currently bringing all these solutions to its products to lead the “new normality” era of face recognition.

¹ We refer the reader to this interesting scientific paper by Blais et al. www.ncbi.nlm.nih.gov

² An introductory read about this topic can be found in the paper “Learn to pay attention” by Jetley et al. arxiv.org/abs/1804.02391

Written by: Isabelle Hupont

In January 16th 2020 Bloomberg issued a short piece of news about the European Commission (EC), which apparently was going to unveil a paper about artificial intelligence (“AI”) in mid-February. According to such news agency, the EC recommended a new regulation including a moratorium of 5 years for facial recognition in public places, even though it was said that the paper final version was likely to change. Let’s try to summarise what we have as objectively as possible:

• A paper, not a law and not even a law proposal, but just an opinion. Of course, the opinion of the EC should be relevant, but in any case, it shall be submitted to the long and annoying European legislative machine, before reaching the stage of law. For instance, the first step of GDPR was an opinion of the EC dated on June 2011, that is to say 5 years (the same term of the alleged moratorium) since the first proposal to the approval of the Regulation, which was indeed very different from the first approach.
• The need of new regulation about the use of AI in sectors such as healthcare and transport, including facial recognition technologies.
• A rumour regarding a 5 years moratorium. Such an idea flies over the news as a possible element of the paper, but no consistent evidence is provided to reinforce it.
• A final version likely to be changed. In other words, the question about the moratorium is in the end sheer speculation.

What can we induce from these data?

Not much. Summing up, EC is evaluating a proposal to rule the use of facial recognition in public areas, but it is difficult to guess which will be its content and even harder to glimpse which can be the finally approved regulation, if any. Within this frame a moratorium of 5 years makes little sense, because, going back to GDPR, 5 years is the time that can elapse before the approval of a future European Regulation in the matter, provided that the legal machine was started up right now.

What has some media induced from these data?

The headlines of a catastrophe, as usual. These are some examples, from DEFCON 2 to Apocalypse:

• Politico: “EU considers temporary ban on facial recognition in public spaces”
• BBC: “Facial recognition: EU considers ban of up to five years”
• MIT Technology Review: “The EU might ban facial recognition in public for five years”
• The Telegraph: “European Commission mulls ban on facial recognition technology”
• Techerati: “Who watches the watchmen? Why Europe is right to ban facial recognition”

How much time was the alarm ringing?

Less that a fortnight we should say, because on January 30th Reuters published that EU drops idea of facial recognition ban in public areas: paper. In case that such idea has ever existed, could we say.

If we dive inside the news, we could find two very interesting things. First of all, the Commission was willing to recommend a specific regulation in some key sectors, by means of a paper expected for February 19th. Secondly, there is a priceless quote from Brad Smith, the president of Microsoft, who says that “a facial recognition AI ban is akin to using a cleaver instead of a scalpel to solve potential problems”.

So, once again, how much time was the alarm ringing?

Being honest, it is still on the run. None of the media that fed the panic have issued the denial, which have been published in other sites. In any case, outside the sector, most of the people do not know that EC is not considering, neither mulling, any kind of prohibition, temporary or not, on facial recognition. On the contrary, there are still news about facial recognition where a ban, which never existed, is still mentioned.

Did finally the EC issue a paper about AI?

Yes, they did. In due time, last February 19th in the morning the EC issued the “White Paper on Artificial Intelligence – A European approach to excellence and trust”. The content of its 27 pages can be briefed in two main objectives. First of all, to support the development of AI in the European Union (“EU”), because the EC deems it a strategical sector to preserve the EU’s economic growth and technological leadership. And secondly, the EC wants to set forth a new legal framework, avoiding the fragmentation of the single market and giving legal certainty to the citizens, in order to protect their fundamental rights (mainly security and privacy), and also to the companies working with AI.

This new legal framework shall cover only those sectors deemed as high risk, including:

• Healthcare
• Transport (especially autonomous vehicles)
• Some public services such as:
  • Asylum
  • Migration
  • Border controls
  • Judiciary
  • Social security
  • Employment services
• Facial recognition

Here it is, the EC report includes a specific, really short section entirely dedicated to facial recognition, from which we can synthetize the next three conclusions:

1. Only facial recognition is deemed as a high-risk sector, that is to say, the identification of a person, for instance in a video surveillance system, against a database of several people. Meanwhile the access control, that is to say the authentication or verification of a person identity against the image of this same person kept in the system, is not deemed as high-risk, therefore it will be spared form this new legislation.
2. There is and open debate to decide in which facial recognition can be used, in fact, the paper is open to public consultation.
3. The legal framework shall be the same for all EU in order to avoid the fragmentation of the single market and to give legal certainty.

Undoubtedly this new regulation will be welcomed by all stakeholders, because GDPR, despite appearances, does not regulate enough the processing of biometric data. Although its article 9.2 sets forth several cases where the use of such data is permitted, none of the following or other exceptions has been duly developed by law afterwards:

• Explicit consent of the data subject.
• Carrying out obligations and exercising specific rights in the field of employment and social security.
• Establishment, exercise or defence of legal claims.
• Reasons of substantial public interest. In the paper, the EC deems this exception as the most suitable for facial recognition.

In conclusion, all stakeholders need, for elemental reasons of legal certainty, a secure path in which facial recognition developers, private security companies or even law enforcement bodies could use the most suitable solutions according to law, and at the same time the citizens’ rights to security and privacy are duly respected. But, of course, this approach, being a consensus solution far away from catastrophe, does not seem to be hot news.

It depends, could we answer, but let’s deal with this question in a Galician style, that is to say asking more questions that will guide us in the application of GDPR.

What is a personal data?

According to article 4.1 of GDPR, “personal data means any information relating to an identified or identifiable natural person (…) by reference to an identifier such as a name, an identification number, location data, an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person.”

Therefore, a personal data is any information relating to an identified or identifiable natural person, but

When a natural person can be deemed identifiable?

According to whereas 26 of GDPR an information will meet the divine stage of personal data where it permits the identification of a natural person with the available technology and at a reasonable cost. Well, we have solved two questions, but some more are still waiting for an answer to understand facial recognition regulation in GDPR.

What are biometric data?

The definition is found at article 4.14 of GDPR: “‘biometric data’ means personal data resulting from specific technical processing relating to the physical, physiological or behavioural characteristics of a natural person, which allow or confirm the unique identification of that natural person, such as facial images or dactyloscopic data.”

Taking into account this, any pack of physical and/or behavioural data allowing to identify uniquely a person is suspected of the crime “biometric data”, in particular if it contains images or fingerprints. Such understanding can be right for fingerprints, but not for images, because without the suitable software the image of a person is not enough to provide a unique and unmistakable identification of such person. Indeed, whereas 51 of GDPR reinforces such view, emphasizing the use of technological means for unique identification instead of the kind of data being used: “The processing of photographs should not systematically be considered to be processing of special categories of personal data as they are covered by the definition of biometric data only when processed through a specific technical means allowing the unique identification or authentication of a natural person.”

So that, a set of images, for instance those obtained in a CCTV system, shall only be included in the category of “biometric data” in case the system uses technology allowing to uniquely identify the persons whose image is reproduced in it. In other words, the images of a CCTV would be considered biometric data where facial recognition tools are applied.

The transcendence of this third question lies in the article 9.1 of GDPR, which forbids the processing of the “special categories of personal data”, including, among others, “biometric data”:

“1. Processing of personal data revealing racial or ethnic origin, political opinions, religious or philosophical beliefs, or trade union membership, and the processing of genetic data, biometric data for the purpose of uniquely identifying a natural person, data concerning health or data concerning a natural person’s sex life or sexual orientation shall be prohibited.”

Therefore, facial recognition falls in the gaol of “special categories of personal data”, being forbidden for all business, as simple as this. So, here we are, facing again the first frightening question:

Can I use facial recognition in my business?

As it is commonly said, when God shuts a door, He always opens a window. Salvation, as usual, comes in the form of several exceptions to the general prohibition, which are contained in the point 2 of the same article 9 of GDPR. Among such exceptions, letters a, b, f and g are especially applicable to facial recognition:

1. “The data subject has given explicit consent to the processing of those personal data for one or more specified purposes”. These terms are suitable for systems where the consent of the enrolled persons is verifiable, especially for control of access, but also for CCTV, in case the system (i) gives enough information and the opportunity to accept to all people acceding the facilities and (ii) does not capture the images of passers-by. In fact, recent Guidelines 3/2019 on processing of personal data through video devices adopted on 10 July 2019 by the European Data Protection Board, states that when explicit consent is alleged for the processing of biometric data, the system shall avoid the capture and processing of “biometric templates of non-consenting persons”.
2. “Processing is necessary for the purposes of carrying out the obligations and exercising specific rights of the controller or of the data subject in the field of employment and social security”. This exemption allows the use of access control facial recognition tools in the workplace, as entrance/exit/presence control of workers or visitors (as opposed to registered workers).

1. “Processing is necessary for the establishment, exercise or defence of legal claims or whenever courts are acting in their judicial capacity”. This letter permits to use facial recognition to prepare, issue or defend against any kind of legal action and are especially applicable for forensic uses, that is to say applying facial recognition to footage in order to identify or track a suspect. Of course, it will be more difficult to justify facial recognition use in real time, only in the basis of this exception, but the system should be GDPR compliant as long as we can prove that (i) it is a necessary mean for identification and (ii) we are using it in a proportional way.
2. “Processing is necessary for reasons of substantial public interest”. This final exception faces us with the last question of this quiz.

What are such reasons of substantial public interest?

Unfortunately, the expression “substantial public interest” is not defined in the GDPR. At least it is clear that some sort of serious public interest should be at stake.

In the case of facial recognition, this public interest fits finely with live facial recognition in CCTV, provided that public security reasons can be alleged. In other words, the system would be GDPR compliant if it is safeguarding any kind of facilities or open spaces facing intense security threats, either because of being a critical infrastructure (chemical industries, power facilities, water supplies, transport means, etc.), because of the amount of people gathered therein (stadiums, concert halls, malls, open crowded areas, etc.), or by any other reason implying a “substantial public interest”.

Summing up, the question can I use facial recognition in my business? may be answered with a yes, instead of an it depends, at least in the following four cases:

1. For access control or even CCTV (i) giving enough information and the opportunity to accept to all people acceding the facilities and (ii) not capturing the images of passers-by.
2. For access control in the workplace.
3. For forensic uses and even in live managing of CCTV systems, in order to prepare, issue or defend against any kind of legal action.
4. For real time CCTV where public security reasons can be alleged.

M80 Radio interviews Javier Rodríguez, Herta’s CEO.
18 April 2017.

Javier Rodriguez Saeta, CEO of Herta Security, shares how they’re using NVIDIA GPUs to train deep neural networks for pattern recognition to help with security at airports, stadiums an train stations.

Telemadrid news reports on how face recognition helps increase safety in soccer stadiums.

From face-blindness to super-recognition

If you hardly ever forget a face, and have no trouble in instantly spotting a stranger within a dense crowd… maybe you are a super-recognizer!

After the London riots of 2011, police forces retrieved thousands of hours of rough, low quality video from surveillance cameras. Where all failed, a single police constable was able to identify more than 180 riotists – all by himself. New Scotland Yard has found about 150 super-spotters like him, among many thousands of police officers from local stations, who periodically attach to Scotland Yard and help them conduct investigations. So far, their amazing identifications have made it possible to capture murderers, molesters and thieves.

We are all different

In the last 15 years, research in cognitive neuroscience has confirmed that the ability to recognize faces varies greatly from one person to another. In fact, this ability appears to fall along a spectrum, running gradually from extremely poor face recognition (prosopagnosia, also called face blindness) to the outstanding skills of super-recognizers, and distributed as a bell among the population [1]. According to psychologists, this ability appears to be innate, and is mostly not learnable. And although no genetic markers have been linked to it as yet, the skill has been found more similar in identical twins rather than parental twins [2], which starts to evidence its heritability.

We look at faces differently than at other objects. It has been shown that some regions in our brains are exclusively dedicated to face recognition [3,4]. And the talent for face-matching (or lack thereof) also transfers to other face analysis tasks, like emotion recognition or age estimation [5]. For instance, super-recognizers seem to be amazingly skilled at recognizing adult celebrities from their childhood (or even babyhood!) photographs. Does not seem that hard to you? Be our guest and try to identify these celebrities (answers are at the end).

Celebrities when they were babies. Can you identify them?

The whole is greater than the sum of parts

So, what is it that confers super-recognizers this superior ability? It does not seem to be linked to a particularly high IQ, nor an extraordinary memory for objects. But super-recognizers do seem to perceive a face differently. Researchers have studied this with the help of eye-tracking techniques. Eye-trackers are often used in psychometric experiments to learn at which image regions we spend more time looking. During face recognition, regular people focus longer on the eyes region, arguably the most informative part in a face, whereas people with prosopagnosia seem to avoid the eyes and instead concentrate on the mouth. Where do super-recognizers spend more time looking? Surprisingly (or not), the nose.

Why the nose? A large body of research supports the idea that the human visual system does not see the face only as a collection of separable features, but as an integrated perceptual whole [6]. Holistic processing would be crucial for face recognition, and super-recognizers would rely more than others on seeing the face as a whole. That would explain their fixation on the centre of a face, from which a global vision can be more easily captured. It would also explain another interesting fact – upside-down faces.

The world upside-down

In a recent study, despite years of experience inspecting passport photos, professional passport officers were no better at matching faces than newbie student participants. They accepted fraudulent photos 14% of the time [7]. However, another recent study concludes that highly-trained forensic experts in face matching do learn specific abilities that are unusual even in natural recognizers, such as identifying faces upside-down [8]. Let the following figure serve as an example. Try to match the frontal face to one of the viewpoints, and you will realize that is much more challenging when upside-down.

Upside-down matching is harder to most of us. [9]

The reason is that we are unable to perceive inverted faces in a holistic fashion. Apparently, trained forensic examiners learn how to break faces into parts, and carefully match them component-wise. So after all, maybe not everything is in the genes.

The social aspect

Falling at the edges of the spectrum can be challenging. Face-blind people identify their couples, friends or relatives by a checklist of attributes: the length of the hair, the color of the eyes, or that mole next to the mouth. Or their gait, voice or clothing (even their shoes!). A face-blind father reportedly dyed the hair of his son when school started, to be able to recognize him at pick up. And Oliver Sacks, the recently deceased famous neurologist who also suffered this condition, admitted he recognized his best friend Eric by his “heavy eyebrows and thick spectacles”. But problems may arise when that woman next to you at the mall has long blonde hair, red lipstick and a brown jacket – just like your wife’s!

Likewise, super-recognition can become socially crippling. Their abilities are so good, that most of the times super-recognizers keep to themselves that they remember people they saw in the past, in order to avoid awkward situations. Just imagine being introduced to someone who claims to clearly remember your face at the entrance of a cinema, three years ago… Creepy.

A useful ability

Recent studies started to shed light on different types of super-recognizers, which could potentially help improving law enforcement and security. For instance, super-memorizers would have superior facial memory skills; super-spotters could be used in the policing of crowds; and super-matchers could be useful for visa verification or passport control.

While prosopagnosia has long received attention from scientists and media, psychologists began to deepen in the other side of the spectrum just a few years ago. The outcomes of their research will undoubtedly shed light on how and why we can perceive and identify faces efficiently. In the meanwhile, most of us, average face-matchers, are happy to rely on super-recognition tools like BioSurveillance.

References

[1] Wilmer, Jeremy B., et al. “Capturing specific abilities as a window into human individuality: the example of face recognition.” Cognitive Neuropsychology 29.5-6 (2012): 360-392.

[2] Wilmer, Jeremy B., et al. “Human face recognition ability is specific and highly heritable.” Proceedings of the National Academy of sciences 107.11 (2010): 5238-5241.

[3] Kanwisher, Nancy, Josh McDermott, and Marvin M. Chun. “The fusiform face area: a module in human extrastriate cortex specialized for face perception.” The Journal of neuroscience 17.11 (1997): 4302-4311.

[4] Duchaine, Brad, and Galit Yovel. “A revised neural framework for face processing.” Annual Review of Vision Science 1 (2015): 393-416.

[5] Russell, Richard, Brad Duchaine, and Ken Nakayama. “Super-recognizers: People with extraordinary face recognition ability.” Psychonomic bulletin & review 16.2 (2009): 252-257.

[6] Taubert, Jessica, et al. “The role of holistic processing in face perception: Evidence from the face inversion effect.” Vision research 51.11 (2011): 1273-1278.

[7] White, David, et al. “Passport officers’ errors in face matching.” PloS one 9.8 (2014): e103510.

[8] White, David, et al. “Perceptual expertise in forensic facial image comparison.” Proc. R. Soc. B. Vol. 282. No. 1814. The Royal Society, 2015.

[9] Original source: http://www.apa.org/science/about/psa/2015/06/face-recognition.aspx

Solutions to the baby quizz

Line 1: Antonio Banderas, Cher, Kurt Cobain, Tom Cruise, Danny DeVito.

Line 2: Leonardo DiCaprio, Michael Douglas, David Duchovny, Kirsten Dunst.

Line 3: Eminem, Angelina Jolie, Jennifer Lopez, Steve Martin.

Line 4: Brad Pitt, Keanu Reeves, Shakira, Sylvester Stallone.

Line 5: Barbra Streisand, John Travolta, Amy Winehouse, Natalie Wood.

Written by: Carles Fernández Tena

If you are into the marketing world, I am sure that you will pay special attention when you hear about “analytics”. Marketers use many different analytics tools to get reports that help them determine the failure or success of their marketing campaigns, and they know that this is the key to success which helps maximize business decisions.

Even programmers and developers who are about to miss a click on this blog post at the simple mention of the word “marketing”. But do you guys really know how analytics can help your work as well? Just imagine the amazing opportunities you have to create applications that can collect and provide such valuable statistics that users will truly love.

How can facial analytics help understanding your clients better?

Face biometrics is useful in as a means of detecting and identifying individuals, but it is also an excellent tool for learning more about your customers.

We are in the era of Hi-Tech. The amount of data you can extract with technology is incomparable to the one you could collect in the physical world. Until now, the only way we could obtain information from our customers was done through human interaction, collecting manually their data through Internet, business cards or asking a few questions. This method only captures information from those customers who buy, but not from those who leave without buying.

Facial analytics enables you to classify individuals based on their physical appearance. This deeper knowledge is very useful, especially if you are also able to know more about not only your current but also your potential customers. And this is what facial analytics is all about: identifying, collecting and classifying information of people going through their daily lives, who breathe and feel – real people.

What can you learn from a face?

Our cutting-edge system allows you to collect information about people walking around your store, or people looking at your marketing displays.

This technology lets you answer questions such as, how many women vs men have been here today? How long have they been for? What product are they more interested in? Which day of the week has more customer affluence?

Facial analytics extract useful information from your clients such as gender, age range, if the person has been there before, if the person wears glasses or if the person is listed as a VIP or in a Blacklist.

But do you know what is the best part of it? That there is no facial recognition, all this information comes only from face detection. Our algorithms allow you to get all this information just by a simple detection, in movement and in real-time.

How can we use all this data?

Imagine you are an owner of a Shopping Mall. It is Tuesday morning and analytics show you that there is a 25 years old woman looking at your display. Automatically the Phillips beard razor ad will switch to a beautifully prepared video of a Swarovski necklace. The girl shows particular interest in the display and jumps into another level to include how nice that necklace would look like if combined with a black handbag, it just takes her breath away.

Imagine now you are the owner of this jewelry shop and you see the exact moment where your VIP client Mrs. White comes into the shop, you will personally assist her and provide her with special and personalized offers. What if you see a person that committed shoplifting before? You will notice and his entrance will be automatically denied.

This permanent acquisition of customer statistics is also useful for example for Christmas Campaigns, for store layout and product positioning, segmentation of the objective target, etc.

So here you have all the reasons why hi-tech facial analytics gives you many and tremendous potential to boost your ROI and open a new path to hi-success.

Written by: Laura Blanc Pedregal