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

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 ?? in depth. The component system clearly outperformed both global systems on all tests.

• Type: Conference
• Year: 2001
• Where: ICCV
• Authors: Bernd Heisele, Purdy Ho, Tomaso Poggio
• Keywords: Computer Vision, ICCV 2001, Single Feature Vector, Single Svm Classifier, Support Vector Machine, Systems Recognize Faces, Viewpoint-specific Svm Classifiers