Anti-spoofing in face recognition-based biometric authentication using Image Quality Assessment

Despite the rapid growth of face recognition-based biometrics for both authentication and identification, the security of face biometric systems against presentation attacks (also called spoofing attacks) remains a great concern. Indeed, Face recognition-based authentication techniques can be easily spoofed using various types of attacks such photographs, videos or forged 3D masks. This work proposes a fast and non-intrusive anti-spoofing solution based on Image Quality Assessment (IQA) and motion cues to distinguish between genuine and fake face-appearances. Quality measures are computed following a novel approach which enables us to highlight these liveness-related motion cues, thus outlining the distinction between real faces and spoofing attacks. Moreover, our method is well suited for real-time mobile applications as it takes into consideration both reliable robustness and low complexity of employed algorithms. Our approach is extensively evaluated on three public databases that include different types of presentation attacks. The obtained results proved to outperform state-of-the-art approaches.