Gender Classification of Human Faces
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This paper addresses the issue of combining pre-processing methods—dimensionality reduction using Principal Component Analysis (PCA) and Locally Linear Embedding (LLE)—with Support Vector Machine (SVM) classification for a behaviorally important task in humans: gender classification. A processed version of the MPI head database is used as stimulus set. First, summary statistics of the head database are studied. Subsequently the optimal parameters for LLE and the SVM are sought heuristically. These values are then used to compare the original face database with its processed counterpart and to assess the behavior of a SVM with respect to changes in illumination and perspective of the face images. Overall, PCA was superior in classification performance and allowed linear separability.
KeywordsDimensionality reduction PCA LLE gender classification SVM
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- 1.A.J. O'Toole, K.A. Deffenbacher, D. Valentin, K. McKee, D. Hu. and H. Abdi. The Perception of Face Gender: the Role of Stimulus Structure in Recognition and Classification. Memory & Cognition, 26(1), 1998.Google Scholar
- 2.B. Moghaddam and M.-H. Yang. Gender Classification with Support Vector Machines. Proceedings of the International Conference on Automatic Face and Gesture Recognition (FG), 2000.Google Scholar
- 3.V. Blanz and T. Vetter. A Morphable Model for the Synthesis of 3D Faces. Proc. Siggraph99, pp. 187–194. Los Angeles: ACM Press, 1999.Google Scholar
- 4.R. O. Duda and P.E. Hart and D.G. Stork. Pattern Classification. John Wiley & Sons, 2001.Google Scholar
- 8.S. T. Roweis and L.K. Saul. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science, 290, 2000.Google Scholar
- 9.L.K. Saul and S. T. Roweis. An Introduction to Locally Linear Embedding. Report at AT&T Labs-Research, 2000.Google Scholar
- 10.B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. Smola and R.C. Williamson. Estimating the Support of a High-Dimensional Distribution. Neural Computation, 13(7), 2001.Google Scholar
- 11.V. N. Vapnik. The Nature of Statistical Learning Theory. Springer, 1995.Google Scholar
- 12.A. B. A. Graf and S. Borer. Normalization in Support Vector Machines. Proceedings of the DAGM, LNCS 2191, 2001.Google Scholar
- 14.H. Jaeger, The “Echo State” Approach to Analysing and Training Recurrent Neural Networks. GMD Report 148, German National Research Center for Information Technology, 2001.Google Scholar
- 15.W. Maass, T. Natschläger, and H. Markram. Real-Time Computing without Stable States: A New Framework for Neural Computation Based on Perturbations. Neural Computation, 2002 (in press).Google Scholar