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Face Recognition with Support Vector Machines: Global versus Component- Based Approach

机译：支持向量机的人脸识别：全局与基于组件的方法

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We present a component-based method and two global methods for face recognition and evaluate them with respect to robustness against pose changes. In the component system we first locate facial components, extract them and combine them into a single feature vector which is classified by a Support Vector Machine (SVM). The two global systems recognize faces by classifying a single feature vector consisting of the gray values of the whole face image. In the first global system we trained a single SVM classifier for each person in the database. The second system consists of sets of viewpoint-specific SVM classifiers and involves clustering during training. We performed extensive tests on a database which included faces rotated up to about 40 deg in depth. The component system clearly outperformed both global systems on all tests.

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作者
Heisele, B. ; Ho, P. ; Poggio, T.;
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年度 2001
页码 1-8
总页数 8
原文格式 PDF
正文语种 eng
中图分类工业技术;
关键词
Pattern recognition; Face(Anatomy); Data bases; Feature extraction; Man machine systems; Vector analysis;

机译：模式识别;人脸（解剖）;数据库;特征提取;人机系统;矢量分析;

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4. Face recognition with support vector machines: global versus component-based approach [C] . Heisele, B., Ho, . 2001

机译：支持向量机的面部识别：全局与基于组件的方法
5. Driver Lane Change Intention Recognition by Using Entropy-Based Fusion Techniques and Support Vector Machine Learning Strategy. [D] . Huang, Xianyi. 2013

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7. Face Recognition with Support Vector Machines: Global versus Component-based Approach [O] . Bernd Heisele, Purdy Ho, Tomaso Poggio 2001

机译：支持向量机的人脸识别：全局与基于组件的方法

Face Recognition with Support Vector Machines: Global versus Component- Based Approach

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