Gender Classification of Human Faces
- 32 Citations
- 2.6k Downloads
Abstract
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.
Keywords
Dimensionality reduction PCA LLE gender classification SVMPreview
Unable to display preview. Download preview PDF.
References
- 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
- 5.L. Sirovich, and M. Kirby. Low-Dimensional Procedure for the Characterization of Human Faces. Journal of the Optical Society of America A, 4(3), 519–24, 1987.CrossRefGoogle Scholar
- 6.M. Turk and A. Pentland. Eigenfaces for Recognition. Journal of Cognitive Neuroscience, 3(1), 71–86, 1991.CrossRefGoogle Scholar
- 7.A.J. O'Toole, H. Abdi, K.A. Deffenbacher and D. Valentin. Low-Dimensional Representation of Faces in Higher Dimensions of the Face Space. Journal of the Optical Society of America A, 10(3), 405–11, 1993.CrossRefGoogle 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
- 13.V. Bruce, T. Valentine and A.D. Baddeley. The Basis of the 3/4 View Advantage in Face Recognition. Applied Cognitive Psychology, 1:109–120, 1987.CrossRefGoogle 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
- 16.M. Baenninger. The Development of Face Recognition: Featural or Configurational Processing? Journal of Experimental Child Psychology, 57(3), 377–96, 1994.CrossRefGoogle Scholar
- 17.D. D. Lee and H. S. Seung. Learning the Parts of Objects by Non-Negative Matrix Factorization. Nature, 401:788–791, 1999.CrossRefGoogle Scholar