Robust Face Recognition Using Block-Based Bag of Words

A novel block-based bag of words (BBoW) method is proposed for robust face recognition. In our approach, a face image is partitioned into multiple blocks, dense SIFT features are then calculated and vector quantized into different codewords on each block respectively. Finally, histograms of codeword distribution on each local block are concatenated to represent the face image. Experimental results on AR database show that only using one neutral expression frame per person for training, our method can obtain excellent face recognition results on face images with extreme expressions, variant illumination, and partial occlusions. Our method also achieves an average recognition rate of 100% on XM2VTS database.