基于直方图均衡化、PCA和SVM算法的人脸识别

Face recognition based on histogram equalization, Principal Components Analysis and Support Vector Machine algorithms

为了提高人脸识别的识别率,本文提出了一种基于直方图均衡化、PCA和SVM算法的人脸识别。首先将人脸图像进行直方图均衡化,这样可以很好的增强图像的对比度。然后使用主成分分析(PCA)对图像进行降维和特征提取,可以减少图像识别的计算量,有效的提高识别的效率。最后,再用支持向量机(SVM)进行分类识别。在ORL人脸数据库上进行了使用验证,表明该方法能提高人脸识别的识别率。

To improve the recognition rate of face recognition, a novel method based on histogram equalization, Principal Components Analysis(PCA) and Support Vector Machine(SVM) algorithms is presented. Firstly, histogram equalization used to the face images, that enhanced the contrast of image. Then, the images were processed by PCA algorithm to reduce dimensionality and extract features, so the calculation amount was reduced and recognition rates were improved. Finally, the SVM algorithm was selected as classifier for the recognition of images. The ORL face database was used to test the proposed method, the experiment results shows that the method can improve the recognition rate.