A novel infrared and visible face fusion recognition method based on non-subsampled contourlet transform

Abstract

Near infrared and visible face fusion recognition is an important direction in the field of unconstrained face recognition research. In this paper, a novel fusion algorithm in (NSCT) domain is proposed for infrared and visible face fusion recognition. Firstly, NSCT is used respectively to process the infrared and visible face images, which exploits the image information at multiple scales, orientations, and frequency bands. Then, to exploit the effective discriminant feature and balance the power of high-low frequency band of NSCT coefficients, the local Gabor binary pattern (LGBP) and Local Binary Pattern (LBP) are applied respectively in different frequency parts to obtain the robust representation of infrared and visible face images. Finally, the score-level fusion is used to fuse the all the features for final classification. The proposed fusion face recognition method is tested on HITSZ Lab2 visible and near infrared face database. Experiment results show that the proposed method extracts the complementary features of near-infrared and visible-light images and improves the robustness of unconstrained face recognition.