Recognition Based on a Combination of Increasing Virtual Samples and Kernel Principal Component Analysis

摘要：

　　In pattern recognition such as face recognition,the recognition result is not only limited by the quality and quantity of samples,but also limited by the extracted principal components.For improving the quality and quantity of training samples and for extracting more efficient principal components,this paper presents a recognition method combing the increased virtual samples and kernel principal component analysis (KPCA),which doubly weakens the influence of nonlinear factors on face recognition.New database is generated with the pose-changed and the mirror-like virtual images.Then KPCA is used for dimension reduction and feature extraction.The shortest Euclidean distance is applied to measure similarity.A series of experiments are conducted in the ORL and YALE face database and the experimental results show the efficiency of the proposed method.

关键词： face recognition virtual sample new database kernel principal component analysis nonlinear factor

作者: Hao Zhang Shunfang Wang

作者单位: School of Information Science and Engineering, Yunnan University, Kunming 650091, China

会议类型: 国际会议

会议名称: 2015 International Conference on Mechanical, Electronics and Information Technology Engineering(ICMITE2015)(2015机械、电子与信息技术工程国际会议)

会议地点: 重庆

会议语种:英文

页码: 522-525

在线出版日期: 2015-03-21（万方平台首次上网日期，不代表论文的发表时间）