关键词: Shearlet变换, 均匀局部二值模式算子, 人脸识别, 图像融合, 协作表示

Abstract:

To extract richer texture features of face images to improve face recognition accuracy, a new face recognition algorithm based on the Shearlet_ULBP features which are extracted by the histogram of Uniform Local Binary Pattern (ULBP) from the Shearlet coefficients, called Shearlet_ULBP CRC (Shearlet_ULBP feature based Collaborative Representation Classification) was proposed. First, Shearlet transform was used to extract the multi-orientational facial information, and the average fusion method was exploited to fuse the original Shearlet features of the same scale. Second, the fused image was divided into several nonoverlapping blocks, and then face image was described by the histogram sequence extracted from all the blocks with the ULBP operator. Finally, the extracted features were fed into the collaborative representation based classifier. The proposed method can extract richer information about edge and texture features. Several experiments were conducted on the ORL, Extended Yale B and AR face databases, more than 99% recognition accuracy was achieved for images without occlusion, while the images are occluded, the recognition accuracy still reached more than 91%. The experimental results show that the proposed method is robust to the illumination, pose and expression variations, as well as occlusions.

Key words: Shearlet transform, Uniform Local Binary Pattern (ULBP) operator, face recognition, feature fusion, collaborative representation