Password-based identification systems have been the industry norm for quite a lot of years now. However, recent research shows how weak these identification and verification systems are. This has put pressure on companies to set up better-regulated security systems. Under this pressure, businesses have found facial verification technology as an easy and smart way for effective security.
Ai verification technology is based on complex artificial intelligence and machine learning technologies, which are constantly evolving. Face verification technology has roots in different kinds of computer approaches, However, at its core, the foundations of the technology lie just a few steps from image acquisition to image verification.
The Working of a facial verification system
Let’s get a little more in-depth about how facial verification technology works. This concept of online face verification is based on an end-to-end facial recognition system.
Face detection
The first step in all face verification systems is to collect images and spot faces in those images. You may submit a picture with a face or multiple faces and the system will identify each one and ask the user to pick the face that requires authentication.
Most facial verification technology systems use ultralight detectors for this process to detect faces even from different angles.
Facial alignment
Effective face scanning makes use of clear facial patterns which are based on a standard system. In real life or in pictures submitted, faces may be at a range of angles displaying different emotions; all expressions would be far from standard. Thus, as soon as the facial data has been collected and accurately identified, the system must work on standardizing the facial pattern. This is face alignment.
For the face alignment step of facial verification technology, systems may use an algorithm called “face landmark estimation” which is responsible for marking different facial elements. These include facial borders, eyes, nose, lips, etc. mapping. Overall, there are about 68 points that are marked during this step.
Face encoding
Once the facial pattern is created, the system then converts this pattern into a computer-readable format. Face verification system converts each point’s location and measurements such as distances between two points into binary or numerical format.
While this may seem like a simple process, it is in fact a very complex one that requires extra attention. The goal during face encoding is to ensure that each face, irrespective of whatever angle it is being shown through, creates the same computerized-facial pattern. Deep learning models are the most common systems for this purpose. Among these, the convolutional neural network or CNN model is the most widely used.
Face matching
In a simple face verification process, the last step involves face matching. This is where the sample image is matched against an image stored in the database. Computer-encoded facial patterns are based on strictly numerical data in which errors are unlikely. Thus, we compare the facial patterns of both images.
Despite there being very little likelihood of error, the computer can still make a mistake and thus, facial verification technology is not 100% accurate. For this reason, we do not require a 100% match from face recognition systems and rather define a threshold number. When the system measures two faces, it calculates the distances between each of the facial landmarks. If the overall distance is greater than the threshold value, then the result is inconclusive or a mismatch.
The two different facial verification task types
Facial verification technology has two main types of applications and tasks that it can perform. These include face identification and face verification.
Identifying similar faces
Face identification involves the process of collecting facial data from an individual in order to find the same person, or similar-looking people, in the database. Face identification methods are one of the most common applications of facial verification technology.
Verifying identities
Through face verification, we compare two different images to determine if they are the same person or not.
In commercial or practical applications, the face verification process is very similar to facial identification. However, facial verification technology goes a step further and verifies the identity of similar-looking people. This happens such that an image is fed into the database which looks for similar-looking people. Once identified, the system, then, confirms whether the person with the highest match, or the lowest distance, is the same as the person in the picture.
Conclusion
As facial verification services advances, its applications will become even more complex and vast. The technology is undoubtedly complicated. However, it is important for those utilizing it to understand the core principles and workings to make the best and maximum use of it.
When it comes to applications, facial verification technology is a legal requirement through KYC regulations. These require the authentication of various parameters for ID verification of customers and employees. KYC face verification is very important for certain businesses to implement and any lack can lead to fines, penalties and bans. In order to avoid such complex situations, facial recognition software companies are actively offering the best services to businesses looking for them.
