Learning Blur Invariant Binary Descriptor for Face Recognition

Binary representations have demonstrated remarkable performance in face recognition for its robustness to local changes and computation efficiency. However, the performance of face recognition based on most binary descriptors are not satisfactory when dealing with blurred face images. To solve this problem, we propose a novel blur invariant binary descriptor for face recognition. Particularly, we maximize the correlation between the binary codes of sharp face images and blurred face images of positive image pairs for learning the projection matrix. After that, we use the learned projection matrix to obtain blur-robust binary codes by quantizing projected pixel difference vectors (PDVs) in the testing stage. Experiment results on FERET and CMU-PIE show that our method achieves better recognition performance than representative binary descriptors LBP and CBFD.