Detection of presentation attacks using imaging and liveness attributes

Abstract

Face biometry is a popular user authentication scheme that is easy to use and tends to be less invasive than other user authentication approaches. Despite the success achieved by face biometrics, face spoofing attacks (or presentation attacks) still pose a challenge to researchers. In practice, fraudsters may deceive a face authentication system by displaying fake copies of an authorised user face, such as photos or videos, and gain unauthorised access to the system. This work proposes a method for detecting unauthorised access attempts using misrepresentations of the identity of an authorised user. The proposed methodology for presentation attack detection relies on the observation of imaging and liveness attributes, such as the detection of liveness using the face deformation energy, and imaging attributes usually found in authentic accesses such as facial and background textures, and steganalysis features. Based on the experimental results, the proposed approach potentially can detect face spoofing attacks at each frame of video sequences with error rates of half total error rate (HTER)= {6.51, 4.93}%, and also in full video sequences with HTER = {5.55, 0}%, for the CASIA and Nanjing University of Aeronautics and Astronautics databases, respectively.