Disguised face recognition via local phase quantization plus geometry coverage

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 phase quantization (LPQ) feature and geometry coverage is presented in this paper. LPQ method is applied to extract the phase statistics feature which is robust to the disguised mode, and 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 different 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 method, the proposed recognition algorithm can achieve high recognition results under disguised conditions.