• 图形图像处理 •

### 基于稀疏化双向二维主成分分析的人脸识别

1. 四川大学 电气工程学院, 成都 610065
• 收稿日期:2018-11-09 修回日期:2019-01-10 发布日期:2019-01-23
• 作者简介:张裕平(1994-),女,硕士研究生,主研方向为图像处理、机器学习;龚晓峰,教授、博士;雒瑞森,讲师、博士。
• 基金项目:
中国博士后科学基金（2017M612958）。

### Face Recognition Based on Sparse Two-Direction Two-Dimensional Principle Component Analysis

ZHANG Yuping, GONG Xiaofeng, LUO Ruisen

1. College of Electrical Engineering, Sichuan University, Chengdu 610065, China
• Received:2018-11-09 Revised:2019-01-10 Published:2019-01-23

Abstract: Two-Direction Two-Dimensional Principle Component Analysis((2D)2PCA) is an improved method of Principle Component Analysis(PCA) in the two-dimensional space.However,just like PCA,the (2D)2PCA is susceptible to abnormal values,its robustness is weak and the extracted feature vectors are non-sparse.So this paper proposes a sparse (2D)2PCA method based on L1 norm,the (2D)2PCA-L1S,to tackle these problems.First,this paper adds the L1 norm constraint to the (2D)2PCA objective function to improve the anti-interference ability.Then,this paper introduces the elastic network constraint into the objective function,so the sparsity is realized by the Lasso and Ridge penalty functions.The following experiments are carried out on the Feret and Yale datasets:the face classification and face reconstruction based on nearest neighbors,and the face recognition based on Particle Swarm Optimization(PSO)-SVM Parameters.The results show that compared with other PCA method,such as the 2DPCA,(2D)2PCA and (2D)2PCA-L1,the proposed method can accurately extract the main face information,and it has better effects on face recognition and face reconstruction.