Meet Us at GITEX Africa
Facia.ai
Company
About us Facia empowers businesses globally with with its cutting edge fastest liveness detection
Campus Ambassador Ensure countrywide security with centralised face recognition services
Events Facia’s Journey at the biggest tech events around the globe
Innovation Facia is at the forefront of groundbreaking advancements
Sustainability Facia’s Mission for a sustainable future.
Careers Facia’s Journey at the biggest tech events around the globe
ABOUT US
Facia is the world's most accurate liveness & deepfake detection solution.
Facial Recognition
Face Recognition Face biometric analysis enabling face matching and face identification.
Photo ID Matching Match photos with ID documents to verify face similarity.
(1:N) Face Search Find a probe image in a large database of images to get matches.
DeepFake
Deepfake Detection New Find if you're dealing with a real or AI-generated image/video.
Detect E-Meeting Deepfakes Instantly detect deepfakes during online video conferencing meetings.
Liveness
Liveness Detection Prevent identity fraud with our fastest active and passive liveness detection.
Single Image Liveness New Detect if an image was captured from a live person or is fabricated.
More
Age Verification Estimate age fast and secure through facial features analysis.
Iris Recognition All-round hardware & software solutions for iris recognition applications.
Complete playbook to understand liveness detection industry.
Read to know all about liveness detection industry.
Industries
Retail Access loyalty benefits instantly with facial recognition, no physical cards.
Governments Ensure countrywide security with centralised face recognition services
Dating Apps Secure dating platforms by allowing real & authentic profiles only.
Event Management Secure premises and manage entry with innovative event management solutions.
Gambling Estimate age and confirm your customers are legitimate.
KYC Onboarding Prevent identity spoofing with a frictionless authentication process.
Banking & Financial Prevent financial fraud and onboard new customers with ease.
Contact Liveness Experts To evaluate your integration options.
Use Cases
Account De-Duplication (1:N) Find & eliminate duplicate accounts with our face search.
Access Control Implement identity & access management using face authorization.
Attendance System Implement an automated attendance process with face-based check-ins.
Surveillance Solutions Monitor & identify vulnerable entities via 1:N face search.
Immigration Automation Say goodbye to long queues with facial recognition immigration technology.
Detect E-Meeting Deepfakes New Instantly detect deepfakes during online video conferencing meetings.
Pay with Face Authorize payments using face instead of leak-able pins and passwords.
Facial Recognition Ticketing Enter designated venues simply using your face as the authorized ticket.
Passwordless Authentication Authenticate yourself securely without ever having to remember a password again.
Meeting Deepfake Detection
Know if the person you’re talking to is real or not.
Resources
Blogs Our thought dumps on all things happening in facial biometrics.
News Stay updated with the latest insights in the facial biometrics industry
Whitepapers Detailed reports on the latest problems in facial biometrics, and solutions.
Webinar Interesting discussions & debates on biometrics and digital identity.
Case Studies Read how we've enhanced security for businesses using face biometrics.
Press Release Most important updates about our activities, our people, and our solution.
Mobile SDK Getting started with our Software Development Kits
Developers Guide Learn how to integrate our APIs and SDKs in your software.
Knowledge Base Get to know the basic terms of facial biometrics industry.
Most important updates about our activities, our people, and our solution.
Buyers Guide
Complete playbook to understand liveness detection industry
In This Post
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.
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.
There are two methodologies of face 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.
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.
The working of face comparison technology can be divided into the following steps;
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.
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;
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.
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.
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
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.
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;
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.
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.
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.
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
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.
In a simple face comparison process flat images are used, whereas a 3D face comparison provides a detailed mold of the person’s face. The technology is designed to assess the bumps and curves which makes it harder to spoof with masks or even makeup. Moreover, 3D Face technology also works perfectly if a person turns his head, unlike other security methods that only work from certain angles. Therefore, 3D face comparison is an enhanced security check.
3D face comparison images are highly accurate in ideal conditions, where precision is almost guaranteed and success rates can soar above 99%. However, such technology can still falter without optimal lighting or when viewing angles are compromised by shadows or extreme head tilts. Even hats obscuring facial features pose a challenge for this advanced system. Despite its superiority over traditional photo comparison methods, this approach is far from infallible and its effectiveness may be impeded in specific scenarios.
Innovative technology like the 3D face comparison plays a major role in security enhancement and thus is directly contributing to fraud prevention. As an example, financial institutions can make use of 3D face comparison technology for identity authentication when a user is trying to access an account or carry out transactions. The system captures facial features and processes them in three dimensions— thereby able to spot any irregularity and differentiate between the actual user and fake pretenders aiming fraud through photos or videos. In this way, it ensures high-level security preventing unauthorized access or fraudulent activities that may lead to loss of sensitive data as well as financial assets.
24 Mar 2025
Fraud Prevention Strategies That Businesses Can Follow in 2025
In 2025, fraud prevention will become more difficult as...
06 Mar 2025
How Deepfake Detection Technolgy Transformed the 7 Major Industries
Deepfake technology is speedily growing from a specific artificial...
05 Mar 2025
Australia Forcing to Implement Age Verification Laws of Social Media
The government has also stressed that any verification processes...
Recent Posts
Replay Attack–How It Works and Methods to Defend Against It
Previous post
Google Set to Crackdown on Deepfake Pornographic Promotional Material
Next post
Comparing Face Verification vs Face Recognition vs Face Identification
Related Blogs