Face recognition techniques and applications are now, more than ever, part of our daily lives. This method of identifying people is a complete revolution in our economies and societies.
This technology is being used for a wide variety of use cases. While we might think that face biometrics is used solely and exclusively to grant access to cell phones and other computing devices, face recognition has a variety of applications.
Not everyone really knows what face recognition is and how it works, how the different systems interact with each other to compose a complete digitized record of our face and how the providers of this technology store this sensitive information.
Even if we don't fall for it, face recognition systems are found in more places than we think.
Face recognition, by definition, is the ability to understand and remember a face, usually human. The technology that makes this possible is face recognition systems, which generally use tools based on artificial intelligence and machine learning algorithms.
In recent years, Face recognition is set as the fastest growing technology and the most widely used to verify the identity of users, customers or employees. Through image systems, video and audiovisual elements, a person's face is captured to generate a unique biometric pattern associated with a person's legal identity.
Face recognition is part of the so-called biometric techniques for identity verification since they measure a living organism and parameterize it to recognize it when it is present. In many cases, we can find face recognition included in the category of biometric security techniques, emerging as the first of them in order of relevance.
Face recognition is used for a variety of purposes. Increasingly, this form of identity verification is used by many companies and institutions for traditional processes in a multitude of use cases.
This technology is applied to new and emerging areas as well as existing ones that are simplified and streamlined thanks to its integration. Among all the possible applications for face recognition, we can highlight the following:
Face recognition stands out for its unobtrusive nature and the simple and inexpensive way to implement it in any use case. The possibilities are limitless and any industry can take advantage of this solution to streamline processes, lower costs and provide a smooth and differentiated experience to its users.
In the case of banking, financial services, insurance and related industries, there are even specific legal regulations governing the use of face recognition to comply with the AML policies to which they are subject because of their activity.
Thanks to the current regulatory framework and the correct implementation of these systems, companies in any industry, including those mentioned above, can perform online face recognition and expand into other markets to operate in them and obtain new customers in a simple way and without additional investment.
This technology has made it possible to transform the current economy and turn sustainable business models into scalable and highly productive ones with only minor modifications to some processes.
To understand how face recognition works we must know all the phases that take place during the recognition process. Although the whole process occurs in milliseconds, 10 procedures are carried out in record time thanks to the advancement of technology.
Broadly speaking, and in a simplified manner, the phases of the face recognition process generally run as follows:
The effectiveness of face recognition is, in the most powerful systems, tested and certified. Like all advances in science and engineering, it responds to hit and miss rates and, fortunately, there are solid and proven face recognition solutions.
It should be noted that, of all the biometric identification methods (fingerprint, voice recognition, iris recognition, etc.), face biometrics is emerging as the most robust and, therefore, the most widespread system at present.
Today, we can state without a doubt that face recognition is a mature, advanced and error-proof technology. As early as 2006, in the Face Recognition Grand Challenge (FRGC), the evaluation of the latest facial recognition algorithms was 10 times more robust and accurate than those used in 2002 and 100 times more accurate than those used in 1995.
Recently, there has been talked about the complications that can arise for face recognition systems after the emergence of deepfakes. Today, face recognition based on AI and machine learning is capable of surpassing the ability of humans, taking into account aspects that go unnoticed by our eyes.
As we have seen, the main purpose of face recognition is to corroborate the identity of a person who is about to perform an action. First, if the person was not registered in the system, initial face recognition was performed to record and store his facial biometric data.
After verification and the creation of an account and credentials, we can ensure that the user who registered as a customer and contracted a product or service is who they claimed to be.
This, which is increasingly being implemented in all industries, although with special attention to those with a high level of risks such as banking, insurance, investments and the like, is necessary to comply with established technical and regulatory requirements.
According to several studies, already in 2016 and in the United States alone, theft associated with identity fraud exceeded a value of $16 billion and affected more than 15 million users. Across the pond, taking the United Kingdom as an example, its fraud prevention organization put the number of phishing cases at more than 170,00.
In this sense, and given the current situation, identity fraud represents one of the most important risks faced by both companies and users. Not only in digital onboarding, but also in subsequent steps.
To give an illustrative example, the theft of online banking passwords to make online payments, bank transfers to other accounts or spy on monetary information is more common than it might seem. All of this is due to the lack of secure and robust strong authentication systems.
Security in the customer journey must be provided in each and every one of its phases, establishing anti-fraud controls and incorporating integral systems that form part of a whole and adapt to all the company's channels. Balancing security and experience would mean giving up fundamental parts of both.
Therefore, having holistic and powerful online face recognition software that integrates across all phases is crucial to ensure a seamless, cohesive and secure experience. Multi-factor authentication systems can dispense with passwords, combining, for example, facial biometrics with SMS OTP codes.