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）

摘要： 为了提高人脸识别效率，提出了一种基于PCA、LDA和SVM算法融合的人脸识别方法。使用主成分分析（PCA）将人脸图像变换到新的特征空间中，消除图像特征间的相关性和噪声，提取人脸全局特征，在实验阶段取较多的投影方向使其尽可能多的保持原始信息；使用线性判别分析（LDA）算法进一步投影变换降低数据维度；使用支持向量机（SVM）分类识别。将PCA、LDA和SVM三种算法的优点结合起来，在ORL数据库上进行仿真实验，结果表明该方法的识别率可达99.0%。

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）