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

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