Image Spoofing Detection Using Local Binary Pattern and Local Binary Pattern Variance

Abstract

Particularly in the field of biometric security using human face has been widely implemented in the real world. Currently the human face is one of the guidelines in the security system. Nowadays the challenge is how to detect data falsification; such an attack is called spoofing. Spoofing occurs when someone is trying to pretend to be someone else by falsifying the original data and then that person may gain illegal access and benefit him. For example one can falsify the face recognition system using photographs, video, masks or 3D models. In this paper image spoofing human face detection using texture analysis on input image is proposed. Texture analysis used in this paper is the Local Binary Pattern (LBP) and Local Binary Pattern Variance (LBPV). To classified input as original or spoof K-Nearest Neighbor (KNN) used. Experiment used 5761 spoofs and 3362 original from NUAA Imposter dataset. The experimental result yielded a best success rate of 87.22% in term of accuracy with configuration of the system using LBPV and histogram equalization with ratio 𝑅 = 7 and 𝑃 = 8.