摘要
文中提出了一种基于支撑向量机的人脸识别方法。该方法与传统方法相比 ,克服了后者固有的过学习和欠学习问题 ,并且对复杂模式的识别能力强 ,达到了很高的人脸识别率。在对训练图像进行预处理之后 ,使用主成分分析方法对人脸图像进行特征提取和选择 ,用所选择的人脸特征向量训练多个支撑向量机 ,最后用训练好的支撑向量机进行人脸识别。文中将支撑向量机性能和传统方法进行了对比 ,并且对不同核函数的支撑向量机的性能也进行了对比。发现当特征脸数量不同时 ,不同核函数支撑向量机的性能也不同。总体而言 ,二阶多项式支撑向量机在人脸识别问题中具有更好的性能。
Support Vector Machine-based method is pre sented to recognize human faces. Compared with traditional methods, such as Nearest Neighbor Rules, Euclidian Distance, Mahalanobis Distance and Neural Networks, this method achieves higher recognition rate. It can classify complicated patterns and overcome disadvantages of overfitting in traditional methods. The process of this methed is as follows: pre-processing the human face images first, then using primary component analysis (PCA) to extract and select the appropriate features of human faces, training multiple SVMs by the face feature vectors, and using the trained SVMs to classify human faces at last. The performance of SVMs are also compared. It is concluded by experiments that the second-order polynomial SVM has better performance than other SVMs on human face recognition.
出处
《红外与激光工程》
EI
CSCD
北大核心
2001年第5期318-322,327,共6页
Infrared and Laser Engineering
基金
教育部科学技术重点项目
上海市"曙光计划"资助
关键词
人脸识别
支持向量机
主成分分析
图像识别
Human face recognition
Support vector ma chine
Primary component analysis
