Face recognition: component-based versus global approaches

Authors:
Bernd Heisele;Purdy Ho;Jane Wu;Tomaso Poggio
Affiliations:
Honda Research Institute US, 145 Tremont St., Boston, MA and Center for Biological and Computational Learning, M.I.T., Cambridge, MA;Hewlett-Packard, Cambridge, MA;Center for Biological and Computational Learning, M.I.T., Cambridge, MA;Center for Biological and Computational Learning, M.I.T., Cambridge, MA
Venue:
Computer Vision and Image Understanding - Special issue on Face recognition
Year:
2003

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Abstract

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 view-specific SVM classifiers and involves clustering during training. We performed extensive tests on a database which included faces rotated up to about 40° in depth. The component system clearly outperformed both global systems.