A Novel Model for Enhanced Principal Component Analysis

摘要：

In this paper, a novel mathematical model for Enhanced Principal Component Analysis (EPCA) is proposed. With the new mathematical model, the performance of EPCA could be enhanced in pattern recognition area. Compared with PCA, EPCA could adaptively distinguish different variables of sample vector according to their scale in statistics. The optimization problem of EPCA could be solved in the framework used to solve the optimization problem of PCA, so EPCA dose not require more computational complexity than other improved PCA algorithms. When applied to face recognition, EPCA are robust to different facial expression, different illumination intensity and large variation in lighting direction. EPCA outperforms many famous algorithms (PCA, FLD and ICA) in the experiments on Harvard face database.

关键词： subspace analysis face recognition principal component analysis

作者: Liu Liyuan Zhang Peng

作者单位: North China Institute of Aerospace Engineering, NCIAE Langfang, China China Mobile Langfang, China

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 384-388

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）