Biometric liveness checking using multimodal fuzzy fusion

In this paper we propose a novel fusion protocol based on fuzzy fusion of face and voice features for checking liveness in secure identity authentication systems based on face and voice biometrics. Liveness checking can detect fraudulent impostor attacks on the security systems, and ensure that biometric cues are acquired from a live person who is actually present at the time of capture for authenticating the identity. The proposed fuzzy fusion of audio visual features is based on mutual dependency models which extract the spatio-temporal correlation between face and voice dynamics during speech production, Performance evaluation in terms of DET (Detector Error Tradeoff) curves and EERs (Equal Error Rates) on publicly available audiovisual speech databases show a significant improvement in performance of proposed fuzzy fusion of face-voice features based on mutual dependency models over conventional fusion techniques.