What Is Facial Recognition

Maybe you’ve boarded a plane using just your biometric data, entered a secure area after a camera recognized your face, or even paid for groceries by simply glancing at a camera. These examples highlight the growing presence of face recognition technology in everyday life.

Face recognition is a type of biometric technology used to identify or verify a person based on their distinct facial features. The concept is straightforward: once an image of a person is obtained, the system scans a database of faces to check for a match. For instance, in payment systems, the bank’s system searches through its clients’ profiles to identify the individual standing at the cashier.

Is Face Recognition The Same As Face Matching?

From a business perspective, face recognition and face matching are different, though one is built upon the other.

Face matching refers to a 1:1 verification process, where the system compares an ID photo with a selfie to confirm if both images are of the same person.

On the other hand, face recognition is about identifying a person from a larger set of data, such as a database, using a single image. Another term for face recognition could be “face search” or “face identification.”

How Does a Face Recognition Algorithm Work in Identity Verification?

The basic face recognition process involves three key steps: detection, analysis, and comparison. An additional step, image quality assessment, can enhance accuracy.

Let’s break it down:

• Detection: The system identifies a face in an image or video using algorithms that locate facial landmarks like the eyes, nose, and mouth. Once detected, the system captures the image for further analysis.

• Quality assessment: This optional step ensures the image meets quality standards, with no obstructions like masks or glasses. It’s usually included in high-level identity verification solutions to improve accuracy, though it may be skipped if the image is taken in controlled conditions.

• Analysis: The system examines the face to create a unique facial signature, or descriptor. These descriptors act as coordinates in a space where similar faces are close together, and different faces are far apart. The closer two descriptors are, the more alike the faces.

• Comparison: The descriptor is then compared with a database of faces. Based on the similarity score, the system determines if the image matches an existing profile. Depending on the thresholds set, the system declares a match or no match.

Integrating face recognition into your identity verification process strengthens protection against unauthorized access to your systems, platforms, and even physical locations. After verifying their identity, a customer’s facial data is stored digitally, so future selfies can be compared to ensure consistency and grant access if there’s a match.

Why Choose Facial Recognition Over Other Authentication Methods?

With the growing risks of phishing, weak passwords, and social engineering attacks, more businesses are embracing passwordless solutions. Biometric technologies, particularly facial recognition, provide a promising alternative for secure, seamless logins.

Examples of Face Recognition’s Game-Changing Potential

Although some traditional uses of face recognition may seem invasive, the technology is proving beneficial in several areas. Here are four impactful use cases:

• Password recovery: Passwords, secret questions, and PINs are easily compromised by scammers. Face recognition simplifies password recovery by allowing users to take a selfie to regain account access securely.

• Event entry/exit management: Large-scale events can pose challenges, such as crowding or fake tickets. Face recognition speeds up identification, reducing congestion and ensuring accurate attendance tracking.

• Security in the sharing economy: Ride-sharing and accommodation platforms benefit from facial recognition by improving authentication processes, reducing fraud, and creating a safer environment for both users and service providers.

• Combating gambling addiction: Face recognition helps individuals voluntarily join self-exclusion programs, blocking their entry into gambling venues. This supports their recovery efforts by preventing access to these environments.

Implementing Face Recognition In Your Workflows

Building a robust face recognition system requires substantial effort. You need to:

1. Develop a reliable face detection algorithm.
2. Train descriptor comparison algorithms for accurate matches.
3. Create a scalable system capable of searching through a large volume of images.

While this is feasible in-house, it can be time-consuming and may not align with your core business expertise.

Regula, with over 30 years of experience in identity verification, offers ready-to-integrate solutions that can accelerate your time-to-market. The Regula Face SDK delivers fast and precise face recognition, liveness detection, and face matching across any device, ensuring that only real, verified individuals gain access to your services, providing unmatched security and confidence.