Maximizing Accuracy and User Experience: The Balance in Face Identity Verification

The digital world changes continuously, and so does the demand for maintaining security against ever-growing threats. Many security measures are available, like PINs, passwords, iris scans, etc. However, face identity verification is increasingly becoming an industrial preference.

Woodrow W Bledsoe’s team experimented with facial recognition with computers about 50 years ago. Back then, the results were unsatisfactory. However, modern-day technology can recognize human faces and serves as a premium security measure against identity thefts.

What Is Face Identity Verification?

Face identity verification validates a person’s identity by comparing facial features with a previously stored image or template. An identity authentication software matches the live face with the photos stored in its database to authenticate if the user accessing the secured resource is the same as who he claims to be.

It is important to note that face recognition is different from face verification. Recognition refers to recognizing the face, while verification involves matching the face to the identity.

Recent advances in machine learning and computer vision have greatly improved the accuracy and reliability of identity authentication softwares. Therefore, they are commonly used in numerous applications like smartphones, office entrances, bank vaults, etc.

How Does It Work?

Face verification works by capturing an image of a person’s face and comparing it with a stored image to determine whether they are the same person. This process involves several steps, including face detection, feature extraction, and matching.

Computer vision algorithms are used to locate the face within the picture. It typically involves finding the edges of the face, identifying facial marks, and cropping the image for a sharper focus.

Next comes the feature extraction stage, which extracts things like the position of the nose, lips, jawline, and distance between eyes. Mathematical techniques and comparisons are used to complete the match and calculate the similarity score.

If the match passes a certain score threshold, then the biometric identity verification software declares the match as accurate, thus providing access to the secured resource or area. Similarly, the system denies access if the similarity score is below a certain threshold.

How To Maximize User Experience While Maintaining High Accuracy Score of Identity Authentication Software?

Maintaining accuracy sometimes takes a toll on user experience. Although, studies hint that the error rate of face identity systems decreased to 0.08% in 2020, and the accuracy has increased to about 99.97%. But still, the balance of the two factors is a significant concern. Here are some practices which may help strike an equilibrium.

• Use High-Quality Images

Appropriate lighting and good imaging equipment lead to high-quality images. Image quality significantly affects the accuracy of identity authentication software, so it is crucial to ensure high-quality images.

• Employ Multi-Factor Verification

Although face identity verification is highly secure, it is better to use additional verification factors like passwords, SMS codes, etc., to maximize accuracy.

You can also use anti-spoofing measures such as liveness detection or eye tracking to prevent fraudsters from using a static image or a video to bypass facial verification.

• Ensure Clear Instructions

One of the significant reasons which leads to accuracy degradation in facial verification is using vague instructions. Therefore, ensure that the instructions are clear and concise so users can take high-quality and appropriate photos which are easier to verify.

• Real-Time Feedback Is Important

Most face verification softwares have real-time indications if the users aren’t positioning right, the images are blurred, or the lighting is poor.

However, it is better to consider other possibilities, like camera distance and lens quality, and ensure that your identity verification service indicates if the requirements are incomplete.

• Monitoring Performance

Computer vision algorithms are continuously improving. Therefore, it is better to keep the performance of your identity verification system in check. Organizations also update their algorithms if the performance of the existing system degrades.

The Takeaway

Face verification is the most used security measure, and its continuously improving success rate escalates digital defenses in all industries.

However, some effort is still needed to pin down the best identity verification software. Various companies are offering facial verification services and softwares. But, when it comes to a reliable source, nothing beats IDefy.