摘要： 提出一种基于二值边缘图像和支持向量机的人脸识别方法，以具有较强光照鲁棒性的二值边缘图像作为人脸表征，用支持向量机来分类。其中二值边缘图像是用一种基于Sobel算子的局部自适应阈值选取边缘检测算法。仿真实验结果表明对于有165幅人脸的Yale人脸库识别率可达92.73%，而对于有798幅人脸图像的AR人脸库识别率可达95.62%，而且该方法对有光照变化的人脸图像有较好的鲁 棒性。

关键词: 二值边缘图像, 支持向量机, 光照变化, 人脸识别

Abstract: This paper presents a face recognition method based on Binary Edge Map(BEM) and Support Vector Machine(SVM). It uses BEM as face representation and employs SVM as face classifiers. The BEM is extracted using Locally Adaptive Threshold(LAT) method. Experimental results show that a face recognition rate of 92.73% can be achieved on 165 Yale face images and 95.62% can be achieved on 798 AR images, indicating that the proposed approach is robust to varying illumination.

Key words: Binary Edge Map(BEM), Support Vector Machine(SVM), varying illumination, face recognition

中图分类号: 

• TP391.41