Research on Improved PCA Face Recognition Algorithm

摘要：

　　To solve the problems of that principal component analysis(PCA)is sensitive to light and affected its recognition rate by external interference factors,this paper puts forward an improved algorithm which based on the fusion of PCA and LDA.Firstly,the input face image is preprocessed with eliminating noise,normalization and gray level distribution equalization.Then using PCA algorithm to project the face training image to obtain low-rank feature subspace,and then the subspace face feature is derived from LDA,thus the fusion feature space is acquired.Finally the training and test samples are projected to the fusion feature space,and identify test samples based on the nearest neighbor rule.The experiment shows that this algorithm can fuse the advantage of PCA and LDA effectively and improve the robustness and efficiency of the system.

关键词： Principal component analysis (PCA) Face recognition Feature extraction Linear discriminant analysis (LDA)

作者: Wei-yi YANG Hui ZHANG Meng-xia LI Fa-nian LAI

作者单位: Faculty of Information and Control Engineering,Shenyang Jianzhu University,China

会议类型: 国际会议

会议名称: 2019 International Conference on Informatics, Control and Robotics 2019信息学、控制和机器人学国际会议(ICICR2019)

会议地点: 上海

会议语种:英文

页码: 213-217

在线出版日期: 2019-06-16（万方平台首次上网日期，不代表论文的发表时间）