基于Shearlet变换和均匀局部二值模式特征的协作表示人脸识别算法

Face recognition algorithm of collaborative representation based on Shearlet transform and uniform local binary pattern

为了获得人脸图像中更丰富的纹理特征以提高人脸识别率,提出了一种基于Shearlet变换和均匀局部二值模式(ULBP)算子提取特征(Shearlet_ULBP特征)的协作表示方法——Shearlet_ULBP CRC用于人脸识别。首先,人脸图像通过Shearlet变换分解,得到多尺度多方向的幅值域图谱,再经过简单的平均融合,获得融合后的幅值域图谱;然后,通过ULBP算子结合分块的方法获得该Shearlet变换后融合图像的直方图特征;最后,结合协作表示的方法来分类所提取到的特征。该方法可以提取到图像更丰富的边缘以及纹理信息,在ORL、Extended Yale B和AR人脸数据库上进行测试,在图像无遮挡的情况下识别率都达到了99%以上,在有遮挡情况下也都达到了91%以上的识别率。实验结果表明,所提方法不仅对于光照、姿态和表情变化具备较强的鲁棒性,同时能在一定程度上处理人脸图像中存在遮挡的情形。

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.