基于支持向量机的人脸姿态判定  

Face pose discrimination with support vector machines

作　　者：叶航军[1] 白雪生[1] 徐光祐[1] 

机构地区：[1]清华大学计算机科学与技术系,北京100084

出　　处：《清华大学学报（自然科学版）》2003年第1期67-70,共4页Journal of Tsinghua University(Science and Technology)

基　　金：国家"八六三"科技攻关项目(863-306-ZT03-01-1);国家自然科学基金资助项目(60273005);高等学校博士点专项科研基金资助项目(9900307)

摘　　要：对于多姿态人脸检测中的姿态判定问题,提出了一种基于支持向量机(SVM)的人脸姿态判定算法。将人脸姿态划分成6个类别,从一个多姿态人脸库中手工标定出1800幅人脸图像作为训练样本集,分别训练基于支持向量分类(SVC)和基于支持向量回归(SVR)2种姿态分类器。另外标定出300幅人脸图像作为测试样本。SVC方法和SVR方法分别取得了1.67%和3.33%的分类错误率。其中SVC方法的分类效果明显优于在传统方法中效果最好的人工神经元网络(ANN)方法(分类错误率为3.33%)。对比实验结果表明,SVM方法对于解决姿态判定问题是很有效的。An approach for face pose discrimination using support vector machines (SVM) is proposed for the multiview face detection. Six different types of poses were defined with 1 800 images from a multiview face database as the training set and another 300 images as the test set. Classifiers based on support vector classification (SVC) had a 1.67% error rate while those based on support vector regression had a 3.33% error rate. The SVC performance was superior to an artificial neural network classifier (3.33% error), which performed best among traditional pattern classifiers. Thus the experiments demonstrated that SVM is a feasible approach for face pose discrimination.

关 键 词：支持向量机 人脸检测 姿态判定 模式分类 人脸识别 智能人机交互 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]