Class-Specific Kernel Fusion of Multiple Descriptors for Face Verification Using Multiscale Binarised Statistical Image Features

This paper addresses face verification in unconstrained settings. For this purpose, first, a nonlinear binary class-specific kernel discriminant analysis classifier (CS-KDA) based on spectral regression kernel discriminant analysis is proposed. By virtue of the two-class formulation, the proposed CS-KDA approach offers a number of desirable properties such as specificity of the transformation for each subject, computational efficiency, simplicity of training, isolation of the enrolment of each client from others and increased speed in probe testing. Using the proposed CS-KDA approach, a regional discriminative face image representation based on a multiscale variant of the binarized statistical image features is proposed next. The proposed component-based representation when coupled with the dense pixel-wise alignments provided by a symmetric MRF matching model reduces the sensitivity to misalignments and pose variations, gauging the similarity more effectively. Finally, the discriminative representation is combined with two other effective image descriptors, namely the multiscale local binary patterns and the multiscale local phase quantization histograms via a kernel fusion approach to further enhance system accuracy. The experimental evaluation of the proposed methodology on challenging databases demonstrates its advantage over other methods.