Abstract: In view of the limitation of the facial feature fetched by Asymmetric Region Local Binary Pattern（AR-LBP） as well as the interclass interference of faces in the Collaborative Representation based Classification（CRC）, a supplementary joining the features of multi-layer AR-LBP and features of Weber Local Descriptor（WLD） is proposed, and the interclass interference is reduced by augmenting the sparsity in CRC. Firstly, the multi-layer AR-LBP features of face images are extracted and cascaded. Then AR-LBP features are integrated with the WLD features extracted from the original image, so the multi-layer AR-LBP and WLD fusion features are obtained. Finally, the Sparsity Augmented Collaborative Representation based Classification（SA-CRC） is used to complete the classification of faces. In the face database of ORL, Yale and GT, the recognition accuracy of fusion features in this paper is increased by 0.7%~42.6% compared with AR-LBP, WLD, and multi-layer LBP and HOG fusion features. When CRC is replaced with SA-CRC, the recognition accuracy is further improved.

Key words: Asymmetric Region Local Binary Pattern（AR-LBP）, Weber Local Descriptor（WLD）, Collaborative Representation based Classification（CRC）, Sparsity Augmented Collaborative Representation based Classification（SA-CRC）, feature extraction

摘要： 针对非对称局部二值模式（AR-LBP）提取的人脸特征有限，以及协同表示分类（CRC）人脸存在的类间干扰，提出以多层AR-LBP特征及联合韦伯局部描述子（WLD）特征进行补充，并以增加CRC中稀疏性来降低类间干扰。提取人脸图像的多层AR-LBP特征并级联，与从原图像提取的WLD特征级联得到多层AR-LBP与WLD融合特征，采用稀疏增强的协同表示分类（SA-CRC）完成人脸分类。在ORL、Yale和GT公开人脸库上，提出的多层AR-LBP与WLD特征融合算法与AR-LBP特征提取算法、WLD特征提取算法以及多层LBP与HOG特征融合算法相比，识别正确率提高了0.7%~42.6%；当利用SA-CRC取代CRC后，识别正确率进一步得到提高。

关键词: 非对称局部二值模式（AR-LBP）, 韦伯局部描述子（WLD）, 协同表示分类（CRC）, 稀疏增强的协同表示分类（SA-CRC）, 特征提取