Face Verification Using Local Binary Patterns and Maximum A Posteriori Vector Quantization Model

Abstract

The popular Local binary patterns (LBP) have been highly successful in representing and recognizing faces. However, the original LBP has some problems that need to be addressed in order to increase its robustness and discriminative power and to make the operator suitable for the needs of different types of problems. Particularly, a serious drawback of LBP method concerns the number of entries in the LBP histograms as a too small number of bins would fail to provide enough discriminative information about the face appearance while a too large number of bins may lead to sparse and unstable histograms. To overcome this drawback, we propose an efficient and compact LBP representation for face verification using vector quantization maximum a posteriori adaptation (VQ-MAP) model. In the proposed approach, a face is divided into equal blocks from which LBP features are extracted. We then efficiently represent the face by a compact feature vector issued by clustering LBP patterns in each block. Finally, we model faces using VQ-MAP and use the mean squared error for similarity score computation. We extensively evaluate our proposed approach on two publicly available benchmark databases and compare the results against not only the original LBP approach but also other LBP variants, demonstrating very promising results.