Computer Engineering and Applications ›› 2013, Vol. 49 ›› Issue (11): 183-186.

### Face recognition method based on 2DPCA-Lp

LI Yong, LIANG Zhizhen, XIA Shixiong

1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
• Online:2013-06-01 Published:2013-06-14

### 基于Lp范数的2DPCA的人脸识别方法

1. 中国矿业大学 计算机科学与技术学院，江苏 徐州 221116

Abstract: Principal Component Analysis（PCA） is a classical method for dimension reduction. 2DPCA is able to extract features fast because it does not transform image matrices into vectors before extracting features. However, both PCA and 2DPCA are often not robust in the case of outliers since they are based on L2-norm. Moreover, the solution to PCA or 2DPCA is generally not sparse. A new method based on L1 norm with Lp-norm constraints is proposed. It can obtain the sparse solution when the parameter p approaches 1. This method is not only fast and convenient just like 2DPCA, but also generic and less sensitive to outliers. Besides, it is proved that this proposed method can obtain a local maximal solution. Some experiments are carried out on ORL and UMIST face data sets to demonstrate the effectiveness of the proposed method.