Learning Angular Reconstruction based Binary Descriptor for Face Recognition

In this work, a simple angular reconstruction based binary descriptor (ARBD) strategy is proposed for face recognition. Compared with previous learning-based hashing methods such as compact binary face descriptor (CBFD) and sparse projection matrix binary descriptor (SPMBD) which measure data similarity with the Euclidean distance, our ARBD focuses on using the cosine similarity to generate binary codes. Specifically, the angular reconstruction term is added to the objective function to minimize the reconstruction error. Furthermore, an efficient algorithm based on the augmented Lagrange method (ALM) is designed to explicitly address the discrete constraint in the Hamming space. We also perform pooling on the learned binary codes with the unsupervised clustering manner to improve their discriminative ability. The results on two face datasets (i.e., CAS-PEAL-R1 and PaSC) demonstrate the superior performance of our ARBD over other existing face recognition methods.