Face spoofing detection using improved SegNet architecture with a blur estimation technique

Biometrics has been increasingly used as the well-known technology for the identification and verification of a person. Among the different biometric traits, the face has been extensively used for human identification and is therefore much vulnerable to face spoofing attacks. In this proposed work, the face is detected with the help of an improved SegNet-based architecture, with blur measure on the basis of local min-max of left and right edges and calculate blur of horizontal and vertical edges. Image filtering is done by an adaptive median filter (AMF). The proposed and novel five-layer encoder decoder SegNet-based algorithm improves the accuracy on various benchmark dataset, i.e., NUAA, replay, printed, CASIA and live database for face liveness detection. The detection rate has reached up to 97% and the time taken for liveness is reduced up to one sec per image. This proposed algorithm shows better value of recall, precision and error rate as compared to earlier algorithms.