Unlocking The Potential of 3D Face Comparison Technology For Enhanced Security

Imagine you are sitting at a family function and suddenly spot someone who looks familiar but cannot recall who that person is. So what do you do? 

Instinctively, most people would start flipping through their old Facebook photo albums, scanning through the faces and comparing. This simple yet engaging act is known as face comparison. This activity might stop exactly where it started for normal people, but this simple act extends beyond personal scenarios. It forms the rudimentary foundation of facial recognition technology and liveness detection solutions. From ameliorating the security mechanism in financial institutions to enhancing customer experiences, ‘Face Comparison’ bridges personal familiarity with broader applications. 

In this blog, we aim to conceptually understand ‘face comparison’, and discuss its type and working mechanics. 

Understanding Face Comparison

• Defining face comparison

Face comparison is a highly advanced technology that aims to analyze and assess facial features from two separate images and then compare them to see how similar they are. 

Face comparison online is often an amalgamation of automated and manual face recognition processes. However, with the advent of AI face comparison technology, we have seen a shift from manual to automated. 

• Classification of face comparison

There are two methodologies of face comparison;

• 1:1 Comparison

1:1 comparison is considered the most common methodology of AI face comparison. In this type, a single face or image is compared against another single image. The purpose here is to verify the identity of the person. 

For example, unlocking your smartphone is a perfect example of a 1:1 comparison, as the recognition software compares your live face with the one stored on your device.  

• According to the NIST benchmark vis-a-vis 1:1 Verification, facial identity solutions must reach an ideal state of “FNMR@FMR=10^6”. 
• This means that the probability of falsely stating two separate samples are from the same person (False Non-Match Rate) should be as low as the value of 10^-6. 
• Secondly, the probability of the system failing to recognize when two samples are from the same person (False Match Rate) should be equally low, i.e. equivalent to the value of 10^-6. This is a regulation that facial recognition solution providers need to comply with.  

• 1:N Comparison

1:N comparison aims to compare a single face against a large database of images to find a match. 

For example, a casino implements a 1:N face comparison to restrict or limit the entry of potential criminals at their premises. The security camera will capture an image of the person entering the casino. This image is then compared to the large database (which is denoted as N in the ratio). The database contains known criminals or people who are banned from casinos. Now, if there is a match, security officials are alerted instantly. 

• According to the NIST benchmark for 1:N identification, the acceptable FNIR (failure to recognize someone) when the FPIR (mistakenly identifying someone else) is set at 0.003. 

How Does Face Comparison Work?

The working of face comparison technology can be divided into the following steps;

1. Face Detection

The first step of face detection consists of finding a face in the image. It is not as easy as it sounds, because there might be multiple variations in the image such as quality, lighting, or even the position of the person whose face needs to be detected. 

2. Facial Feature Extraction

After the face is located, the next step consists of focusing on the facial features. The facial features are the unique identifiers of the person which distinguish him or her from the crowd. These may include;

• Distance between the eyes 
• Facial thirds
• Chin projection
• Tall or short ramus
• Hairline
• Shape of the eyebrows
• Lips (full or thin)

3. Face Mapping

Once the feature extraction process is completed, the software system creates a map of the face with all the elements mentioned above kept intact in the single image. So this “map” is a code that is represented by the facial geometry of the person. 

4. Face Comparison

After the face mapping step, the actual face similarity process starts. As mentioned earlier in the categories section, 1:1 and 1:N are the two main types of face comparison. So post-face mapping, the software compares the facial code of the unknown face against a signal image or against a large database of faces to find the match. 

5. Match Scoring

After the faces are compared, the software produces a “similarity score” between the two compared faces. The purpose of this score is to express the similarity of the images in percentage. The statistical representation, if high or low, will indicate whether the face is the same or different. 

Read More: Face Matching Technology Future of Secure Authentication

Use Cases of Face Comparison In The Facial Recognition Industry

In the facial recognition industry, face comparison caters to just one aspect by answering this one the question, ‘Is it the same person?”. It does not answer “Who is it?” as we see in the facial recognition procedure. This simpler task helps us to utilize face computer technology in a wide variety of powerful applications. Let’s discuss some use cases of this tech.

1. Ameliorated Security

The face similarity checker augments an additional layer of security for access. So if a person tries to access a server room, he may have to follow a three-step process;

• First, swipe the keycard.
• After that, enter the personal PIN code
• And then undergo a face comparison to confirm truly that it is you.

If major high-security areas start using this Multi-Factor authentication (MFA) process it will diminish the risk of unauthorized access and prevent people from stealing credentials. 

2. Fraud Prevention

Fraudulent activities have significantly risen in the past decade. Recent statistics shared by a survey stated that 60% of credit card holders in America have been victims of identity fraud numerous times. Moreover, around 50 million US citizens were charged with unauthorized purchases and this amount surpassed $4 billion.

To prevent such frauds from taking place, face-comparing technology can be used. Whenever someone opens a new bank account, online or not, the facial comparison will verify the applicant against their government-provided National Identification Cards. The live system will compare the selfie to the person’s image on the ID. This robust security mechanism will prevent identity theft and diminish the chances of a culprit creating a fraudulent account. 

3. Border Security

Apart from Fraud prevention, face comparison technology can be used to enhance border security. At airports, bus stations, train stations, and sea ports this technology can be utilized to compare the traveler’s face with the stored image in their passport chip. This will not only ensure a safe verification process but will be quick too. The U.S. Department of Homeland Security’s U.S. Customs and Border Protection program is improving its financial recognition technology. If they incorporate face comparison and use its 1:N methodology it would enable a more efficient security process. 

Beyond 2D: Why Facia’s 3D Face Comparison Is The Future

The 2D facial recognition technology has offered many applications but given the diversity of the prevalent spoofing attacks, it has yet to offer holistic solutions. This creates the potential for 3D face comparison that can revolutionize the industry where facial recognition systems are required. 

As we know the world is 3d and not just a flat image. 2D recognition cannot perform well when there is a variation in lighting, pose, or even facial expression. In this scenario, 3D technology can grasp the depth and contours of the face like nothing else. It not only creates an exact representation of the original image but leads to a very low False Match Rate (FMR). 

Our Facial recognition solution, Facia, utilizes 3D technology to offer various advantages to its users. For instance, it has made Liveness Detection very easy. By using 3D facial recognition technology it can analyze the subtle movements of the face and detect any change that may take place in depth. This adds another layer of security that 2D needs help with. Moreover, Facia’s 3D system offers personalized experiences in retail stores that are based on facial recognition. This technology is also able to reflect on the emotional state of an individual, which is something unprecedented. Therefore, it wouldn’t be too far-fetched to claim that Facia is at the forefront of creating groundbreaking facial recognition solutions. 

Read More: Face Morphing Attacks The Threat to Digital Identity and How to Stop Them

Wrapping Up

Conclusively, face comparison technology is not just about providing security. The technology offers diverse use cases as mentioned in this article. By focusing on personalized experience this tech can transform the user experience. So choose Facia as your facial recognition solution as it is on its way to shaping the future where face comparison is both secure and beneficial.