Face recognition with support vector machines: global versus component-based approach
- 13 November 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 2, 688-694
- https://doi.org/10.1109/iccv.2001.937693
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 viewpoint-specific SVM classifiers and involves clustering during training. We performed extensive tests on a database which included faces rotated up to about 40/spl deg/ in depth. The component system clearly outperformed both global systems on all tests.Keywords
This publication has 16 references indexed in Scilit:
- Beyond eigenfaces: probabilistic matching for face recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Support vector machines for 3D object recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1998
- Face recognition by elastic bunch graph matchingIEEE Transactions on Pattern Analysis and Machine Intelligence, 1997
- Automatic interpretation and coding of face images using flexible modelsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1997
- Eigenfaces vs. Fisherfaces: recognition using class specific linear projectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1997
- Support-vector networksMachine Learning, 1995
- Human and machine recognition of faces: a surveyProceedings of the IEEE, 1995
- Face recognition: features versus templatesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1993
- Low-dimensional procedure for the characterization of human facesJournal of the Optical Society of America A, 1987
- An Algorithm for Vector Quantizer DesignIEEE Transactions on Communications, 1980