Face Liveness Detection Based on Perceptual Image Quality Assessment Features with Multi-scale Analysis

Vulnerability of recognition systems to spoofing attacks (presentation attacks) is still an open security issue in the biometrics domain. Among all biometric traits, face is exposed to the most serious threat since it is particularly easy to access and reproduce. In this paper, an effective approach against face spoofing attacks based on perceptual image quality assessment features with multiscale analysis is presented. First, we demonstrate that the recently proposed blind image quality evaluator (BIQE) is effective in detecting spoofing attacks. Next, we combine the BIQE with an image quality assessment model called effective pixel similarity deviation (EPSD), which we propose to obtain the standard deviation of the gradient magnitude similarity map by selecting effective pixels in the image. A total number of 21 features acquired from the BIQE and EPSD constitute the multi-scale descriptor for classification. Extensive experiments based on both intradataset and cross-dataset protocols were performed using three existing benchmarks, namely, Replay-Attack, CASIA, and UVAD. The proposed algorithm demonstrated its superiority in detecting face spoofing attacks over many state of the art methods. We believe that the incorporation of the image quality assessment knowledge into face liveness detection is promising to improve the overall accuracy.