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Facial Recognition: Transformation in Fraud Prevention Strategies

facial recognition
Image source: Flickr | Facial Recognition: Transformation in Fraud Prevention Strategies

Artificial intelligence and machine learning, present in facial biometrics solutions, are technologies that have changed the game in terms of fraud prevention. With increasingly intelligent algorithms, facial recognition has become a powerful protection tool for companies that need to be sure who is on the other side of a commercial transaction.

It's no secret that we are living in an increasingly connected society, in which businesses in digital environments grow exponentially and gain new followers all the time. Along with this expansion came the benefits of a more agile and practical life, but also countless concerns and precautions regarding the security of the information we share at all times.

Facial recognition technology gained traction in the market, at the same time that fraud protection and prevention strategies began to target the uncertainty that, on the other side of the screen, was the same person they said they were. Today, the best facial recognition algorithms improve automatically every moment, based on a lot of analytical intelligence and machine learning.

In this article, you will be able to learn a little more about the concept of facial recognition and the most efficient solutions on the market for biometric validation. You will also be able to understand how this technology should occupy an even greater space in the market with the increase in companies' demand for protection against today's biggest business risks - such as biometric forgery through the dissemination of methods such as deepfakes and realistic masks in 3D, for example. Good reading!

What is facial recognition and how does it work?

Facial recognition is a technology that uses the neural network algorithm (computational intelligence) to make a mathematical mapping of a person's facial characteristics - such as the distance between the eyes, the shape of the face, and the dimensions of the nose and mouth. By recording this information, it can identify and authenticate the individual so that they can access restricted areas or log into websites and applications with high-security requirements.

The image is captured through a selfie (taken by smartphone or computer) and the access attempt is validated in real-time, in comparison with a database of images that are reliable and already validated – they may be the basis of the image itself. company or belong to a qualified data bureau such as Serasa Experian. The comparison result is given with high accuracy and speed, generally without any point of friction with the user experience.

Facial recognition works through a biometrics solution, which matches the information that is entered in real-time into the system and the image that is present in the file of trusted faces. A score is given for the similarity between the two images, generating a 'similarity score' that will support the algorithm's decision. This is how Facial Biometrics software guarantees that a person is the same person they claim to be – and, in this way, we can know who is who!

Where is facial recognition used? What are the applications?

The use of facial recognition is mainly associated with issues related to security: identification of people in public spaces (including those missing or those wanted by the courts), monitoring high-crime regions, controlling access to spaces with restricted circulation, validating of financial or commercial transactions, unlocking smartphones and computers, entry into large events (such as concerts or football games), user authentication on secure platforms and private security systems (such as employee access to the company network or entry/exit of residents in a condominium), among countless other applications.

Looking at the more unusual ones, we highlight two types of use of facial recognition: the analysis of consumer behavior in shopping centers and large retailers for more targeted personalization of offers and the monitoring of emotions (EDR) and facial expressions to analyze momentary feelings – from a purchasing behavior trend to a dissatisfaction scenario that indicates the necessary action from a mediator.

Facial recognition versus deep fakes

Biometrics solutions that perform facial recognition can prevent the spread of deepfakes through technological intelligence. The more fraudsters study ways to deceive facial recognition, the more deep learning algorithms are updated to block any attempts of fraudulent origin. The most common example at the moment is the manipulation of images and voices of famous people in videos, in which they appear to talk about topics (almost always controversial) that they have never talked about. In this case, a scan carried out by a facial biometrics solution can detect disconnected movements or deformities of the face resulting from image manipulation, revealing the existence of false digitally assembled content.

Still on the battlefield between fake videos and biometric recognition, when someone submits material claiming to be that person, solutions with more reliable algorithms are capable of reliably validating whether, behind that face, an authentic person or software is trying to emulate the existence of a third party. Not all solutions available on the market may indeed be ready to identify deepfakes in 100% of situations; Therefore, companies' prevention strategy must be capable of connecting several anti-fraud layers so that business protection is fully effective.

As we are talking about technology and the transformative power it has at every moment, preventing fraud with deepfakes must be seen as a process in continuous evolution. It's almost a race against time: fraudsters are constantly working to circumvent software that performs facial recognition and identifies fraud, while prevention solution providers must continue to improve their concepts and algorithms to prevent scams from evolving.

Is facial recognition a secure technology?

Yes, facial recognition is one of the most advanced and robust technologies for preventing fraud using images or biometric information, as it is a solution developed based on data science and a lot of analytical intelligence. In addition to recognizing faces with high accuracy, the technology is capable of performing life tests and easily identifying the incidence of a fraud attempt through the use of static images, scenes manipulated via software, or the use of realistic masks.

The constant evolution of algorithms and situations such as the end of the mandatory use of masks post-pandemic helps to minimize a point of attention that has always been linked to facial recognition: the inaccuracy of identification in adverse conditions. When the face is partially hidden by shadows, sunglasses or masks, the solutions find it more difficult to draw the comparison and tend not to validate the similarity of the person with the data present in the base. Another critical case is the recognition of adverse lighting conditions, both about the low incidence of light in the location and its excess.

There is also a point that covers security and raises doubts about the use of facial recognition technologies: legal regulation and ethical issues involving the collection and use of a person's images. With the LGPD (General Personal Data Protection Law), there is a requirement for transparency, and the holder must be notified that their data will be collected, but, in situations such as fraud prevention, there is no need for consent from each user – it is only necessary to ensure that the purpose of the use is being fulfilled and that the data is stored in complete security.

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