Local Operators and Measures for Heterogeneous Face Recognition

Abstract

This chapter provides a summary of local operators recently proposed for heterogeneous face recognition . It also analyzes performance of each individual operator and demonstrates performance of composite operators. Basic local operators include local binary patterns (LBP), generalized local binary patterns (GLBPs), Weber local descriptors (WLDs), Gabor filters, and histograms of oriented gradients (HOGs). They are directly applied to normalized face images. The composite operators include Gabor filters followed by LBP, Gabor filters followed by WLD, Gabor filters followed by GLBP, Gabor filters followed by LBP, GLBP and WLD, Gabor ordinal measures (GOM), and composite multi-lobe descriptors (CMLD). When applying a composite operator to face images, images are first normalized and processed with a bank of Gabor filters and then local operators or combinations of local operators are applied to the outputs of Gabor filters. After a face image is encoded using the local operators, the outputs of local operators are converted to a histogram representation and then concatenated, resulting in a very long feature vector. No effective dimensionality reduction method or feature selection method has been found to reduce the size of the feature vector. Each component in the feature vector appears to contribute a small amount of information needed to generate a high fidelity matching score. A matching score is generated by means of Kullback-Leibler distance between two feature vectors. The cross-matching performance of heterogeneous face images is demonstrated on two datasets composed of active infrared and visible light face images. Both short and long standoff distances are considered.