Two-Stage Fusion of Local Binary Pattern and Discrete Cosine Transform for Infrared and Visible Face Recognition

Abstract

Combination the corresponding advantage of infrared and visible images, the fusion of visible-light and near-infrared features is becoming one of the key research directions of uncontrolled face recognition. In this paper, a new face fusion recognition method based on near-infrared and visible-light imaging is proposed to make full use of the complementarity in two modalities. Firstly, for near infrared imaging, the low frequency part is retained for discrete cosine transform (DCT) coefficients and the block histogram of local binary pattern (LBP) is jointly applied to represent the discriminative features. Then, LBP features from visible faces are gotten to make up the incapable of local representation in near-infrared imaging. Finally, the three features are sent to the novel two-state fusion module to leverage the performance of face recognition. The experimental results show that the combination of DCT and LBP can extract the complementary features from infrared and visible images, and the two-stage fusion strategy lifts the robustness of the multi-modality face recognition. Especially, the recognition rate of the proposed fusion recognition algorithm has been improved significantly, with regard to the other methods based on statistical feature fusion under the circumstance of small samples.