Disguised face recognition based on local feature fusion and biomimetic pattern recognition

Abstract

Disguised face recognition (FR) is considered as one of the difficult and important problems in FR field. Rather than disguised modeling, a disguised face recognition algorithm based on local feature fusion and geometry coverage is presented in this paper. Local binary pattern (LBP) and local phase quantization (LPQ) is firstly applied to extract the binary and phase statistics features which are robust to the disguised mode, then hyper sausage neuron based on biomimetic pattern recognition (BPR) theory is adopted to construct high-dimensional geometry coverage of different classes, which makes full use of continuous characteristics of identical class face features while avoids the interruption of the disguised mode. Experiments on AR face database and disguised face database established by police face combination software show that, compared with the state-of-the-art methods, the proposed recognition algorithm can achieve high recognition results under disguised conditions.