Face Liveness detection deals with biometric security and privacy. Imagine you open your iPhone and use your Face ID, or use your Google Pixel phone to get access to your phone. Then you place your photo in front of the phone camera, wondering whether it will cause your phone to unlock or not. But the phone vibrates and gives you a short message that pops up on the screen saying, “Could not recognize your face,” and prompts you to use other verification methods. What was happening behind the phone camera? It was the face liveness detection (a.k.a. face anti-spoofing) algorithm in action that safeguards your privacy. The algorithm works simply by making sure that the face presented in front of it is the face of a real human and not a photo, a video played on the camera, or some 3D mask.
But how does the face liveness detection algorithm work? But before digging into the complex technicalities of the algorithm, we first need to understand the ways that the impersonator can use to gain access to our phones or other portable accessories that use face unlock-based technology.
So here we first need to understand between the two terminologies. Face recognition and face liveness detection. In simple words, face recognition helps your phone to identify whether it is you or someone else. You might have seen your phone gallery, where it shows your face along with other faces that you took a photo of sometimes. When you click on it, it will show the relevant photos. That’s a face recognition algorithm in action, which identifies your face and other people’s faces in the photo. So a face recognition system only identifies or differentiates between people. However, it cannot tell you whether the person is real or an impersonation of someone. That’s the reason why modern face recognition systems require a face liveness detection capability.
Now that we have made a distinction between face recognition and face liveness detection, lets dive deeper into the topic and see where face recognition will require help from face liveness detection. There are many ways in which a face recognition system can be compromised and the face liveness detection has to identify them to safeguard the face recognition system. For example, commonly used face attack types, often discussed in biometric community, are printed photo attacks, cut-photo attacks (where eyes and mouth part of printed photos are cut), replay-photo attack (where a person present a photo on the digital media to the camera), replay-video attack (where someone present a video played on the tablet in front of the camera), and the 3D mask attacks. A face liveness detection system aims to distinguish between the real face and these types of attacks.
There are various algorithms that have been developed that are either hardware-based or software-based. The hardware-based solution includes using infrared cameras, thermal cameras, 3D-depth cameras, etc. But since these solutions come with a high cost, the software-based solutions are mostly preferred. The software-based solutions are mostly based on using a combination of signal processing techniques, machine learning, and deep learning techniques.
I feel that’s a lot to digest. Therefore, I will stop here. In the next posts, I will describe how to design a system for face liveness detection from traditional signal processing to using machine learning and deep learning. I will also talk about how one can leverage the Large Language Models (LLMs) for creating a more interactive and context-aware face liveness detection system.
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