关键词: 人脸识别, 主成分分析（PCA）, 线性判别分析（LDA）, 支持向量机（SVM）

Abstract: In order to improve the efficiency of face recognition, this paper proposes a face recognition method based on the fusion of PCA, LDA and SVM algorithms. The Principal Component Analysis（PCA） is used to transform the face image into a new feature space, which eliminates the correlation and noise between the features of the image and extracts the global feature of the face. In the experiment stage, this paper takes more projection directions to keep the original information as much as possible. Then the Linear Discriminant Analysis（LDA） algorithm is used to further project transform to reduce the data dimension. Support Vector Machine（SVM） is used to classify and recognize. In this paper, the advantages of PCA, LDA and SVM algorithms are combined and simulated on the ORL database. The results show that the recognition rate of this method can reach 99.0%.

Key words: face recognition, Principal Component Analysis（PCA）, Linear Discriminant Analysis（LDA）, Support Vector Machine（SVM）