ISSN 1000-1239 CN 11-1777/TP

• 人工智能 •

### 融合整体与局部特征的低秩松弛协作表示

1. (燕山大学信息科学与工程学院 河北秦皇岛 066004) (ysuzhangpan@sina.com)
• 出版日期: 2014-12-01
• 基金资助:
基金项目：国家自然科学基金项目(61071200);河北省自然科学基金项目(F2014203076)

### Low-Rank Relaxed Collaborative Representation Combined with Global and Local Features for Face Recognition

Zhang Pan, Lian Qiusheng

1. (School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004)
• Online: 2014-12-01

Abstract: Face recognition is among the most popular biometric approaches due to its low intrusiveness and high uniqueness. However, recent face recognition algorithms such as sparse representation-based classification algorithms, often ignore the noises during training process. When training samples are corrupted, recognition performance will significantly degenerate. Therefore, face recognition is an active yet challenging topic in computer vision application. To solve this problem, this paper proposes a novel low-rank relaxed collaborative representation algorithm which is combined with global and local features. The low-rank decomposition algorithm separates sparse noise from training samples to get more effective face information. Suppressing the effect of sparse noise is essential for face recognition system. Meanwhile, considering the fact that the different features in a sample should contribute differently to the pattern classification, this paper uses relaxed collaboration representation to get more discriminating coding coefficients. The discrimination of face recognition system will be enhanced. In addition, combining local features with global features can further improve the recognition rate. The proposed algorithm contains all of the above advantages. Experimental results on face databases show that, although both training and testing image data might be corrupted due to occlusion and disguise, the proposed algorithm is very competitive with state-of-the-art recognition approaches on effectiveness and robustness.