Face verification using temporal affective cues

It is widely accepted that biometric authentication systems based on human faces are vulnerable to spoofing attacks, in which attackers exploit recaptured photos of legitimate users to gain unauthorized access. To address this vulnerability, numerous approaches which employ complex physics-based models to verify the liveness of presented images have been proposed. While capable of distinguishing between real and recaptured images, a key weakness of these methods is their reliance on an appropriate choice of the model used. Here, we adopt a fundamentally different approach to detect recaptured images. Our method uses randomized temporal affective cues in the form of facial expressions to verify the liveness of users. A quantitative evaluation user study involving 6 users demonstrated the feasibility of our method, and we have also shown that the system is capable of achieving an average classification accuracy of 95.85% for different facial expressions.