Facial verification technology is a modern security and identity verification technology that utilizes complex computer models. The facial verification industry is booming. While the research field regularly introduces newer ideas to the domain, the industry is proactive in the commercialization of this research. Applications of facial verification are plentiful for almost all kinds of businesses. However, all these applications boil down to the way the technology is being used.
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Looking at a range of industries, we find that most require facial verification today for secure systems. For instance, from airport security to document attestation and KYC face verification, there are hundreds of applications of the technology.
In order to better understand the technology, we take a step back and classify all these applications into three types of use cases or technologies. These are.
Identity verification is the simplest of facial verification systems. This process is straightforward and only involves the matching of faces.
Identify facial verification works such that the system is fed with two faces. The system only has to process these two faces, and that’s it. For example, let’s say you have an account at a bank. The bank uses facial verification for payment withdrawals. Now, when you go to the ATM to withdraw some payment, the system will take a picture of you. This picture will then be processed and compared to your file photo stored within the bank’s database. The face verification system here only requires the computer to deal with two pictures. Such a system is called 1:1 facial verification processing.
Applications of identity verification involve every situation that requires straightforward processes. These include accessing bank accounts, administrative databases, unlocking phones, tax filing, etc.
Analyst-driven facial search refers to searching for a face in a database of other facial images. Unlike identity verification, the facial image is the primary mode of search.
For analyst-driven facial verification, there is a database of faces that have been processed previously. Processing here refers to the analysis and mapping of all facial features.
Law enforcement agencies typically collect all kinds of information from convicted individuals. This information can include facial scans. After face scanning of all individuals, their shots and data is uploaded to a database. Typically, security and investigative agencies have centralized databases that can be accessed by all permitted individuals within the department irrespective of their location. For example, for an institution like the FBI, the facial verification database will be accessible internationally by all teams who need it.
For example, let’s assume a case of a criminal investigation. In criminal investigations sometimes a facial shot of an individual is all we have. In order to look for that particular individual, forensic investigators use facial verification. They upload this shot to the database to find a match among hundreds or thousands of other faces. The system only produces similar matches, and the analyst then has to approve or disprove of the pictures after a manual review. This is manual identification for facial verification. Such face recognition services are thus 1:N processing systems and are in use by law enforcement agencies.
Applications include:
Automated search for facial verification refers to scanning an overwhelming number of faces to find one that matches a particular template.
Facial verification deals with high-throughput applications. These include but aren’t limited to traveler screening, surveillance systems, or other video analytics.
For example, let’s consider the analysis of hefty data for the recognition of a victim of human trafficking or child sexual abuse with illicit videos on the internet. Criminal investigators use pictures of victims to identify victims in video shots where they may likely be found. This also goes vice versa and investigators can use a picture of a victim or dead body to confirm their identity. Screening of perpetrators to identify them in such videos online is also essential and uses automated facial verification methods.
Here, the system does not only match the faces but rather determines a similarity threshold value for each facial template. The system reports a return for all faces that hit the threshold value.
Automated identification for facial verification works on the principle of 1:N+1, and its applications are typically associated with surveillance and law enforcement.
Facial verification is in widespread use today. From security agencies to airports and banks, all major institutions are incorporating face verification systems to improve their processes. This is leading towards industry trends of complex and advanced scientific research building towards security, accuracy, and efficiency.
Facial recognition software companies today are making the most out of these shifting industry trends. However, in spite of the broad range of applications we see around us, the broader use cases of the technology are primary only three. These use cases are how facial verification is working behind closed doors to offer efficient solutions for enterprises today.
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