Kernel Linear Collaborative Discriminant Regression Classification for Face Recognition Using Local Binary Pattern

Binary feature descriptors have been widely used in computer vision field due to their excellent discriminative power and strong robustness, and local binary patterns (LBP) and its variations have proven that they are effective face descriptors. However, the forms of such binary feature descriptors are predefined in the hand-crafted way, which requires strong domain knowledge to design them. In this paper, we propose a simple and efficient Kernel Linear Collaborative Discriminant Regression Classification (KLCDRC) feature learning method to learn a discriminative binary face descriptor in the data-driven way. Firstly, similar to traditional LBP method, we extract block based feature vectors by computing and concatenating the difference between center patch and its neighboring patches. Then learn a feature mapping to project these pixel difference vectors into low-dimensional binary vectors. Lastly, we cluster and pool these projected binary codes into a histogram-based feature that describes the co-occurrence of binary codes. And we consider the histogram-based feature as our final feature representation for each face image. We investigate the performance of our KLCDRC-LBP, KLCDRC and LCDRC on ORL and YALE databases. Extensive experimental results demonstrate that our KLCDRC descriptor outperforms other state-of-the-art face descriptors.