Abstract
The aim in this paper is to show how to discriminate gender using a parameterized representation of fields of facial surface normals (needle-maps). We make use of principle geodesic analysis (PGA) to parameterize the facial needle-maps. Using feature selection, we determine the selected feature set which gives the best result in distinguishing gender. Using the EM algorithm we distinguish gender by fitting a two component mixture model to the vectors of selected features. Results on real-world data reveal that the method gives accurate gender discrimination results.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Burton, A.M., Bruce, V., Dench, N.: What’s the difference between men and women? Evidence from facial measurement. Perception 22, 153–176 (1993)
Bruce, V., Burton, A.M., Hanna, E., Healey, P., Mason, O., Coombes, A., Fright, R., Linney, A.: Sex discrimination: how do we tell the difference between male and female faces? Perception 22, 131–152 (1993)
Marr, D.: Vision. W.H. Freeman, San Francisco (1982)
Smith, W.A.P., Hancock, E.R.: Recovering Facial Shape and Albedo using a Statistical Model of Surface Normal Direction. In: Tenth IEEE International Conference on Computer Vision, vol. 1, pp. 588–595 (2005)
Fletcher, P.T., Joshi, S., Lu, C., Pizer, S.M.: Principal geodesic analysis for the study of nonlinear statistics of shape. IEEE Transactions on Medical Imaging 23, 995–1005 (2004)
Pennec, X.: Probabilities and statistics on riemannian manifolds: A geometric approach. Technical Report RR-5093, INRIA (2004)
Figueiredo, M.A.T., Jain, A.K.: Unsupervised Learning of Finite Mixture Models. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(3) (2002)
Figueiredo, M.A.T., Leitão, J.M.N., Jain, A.K.: On Fitting Mixture Models. In: Hancock, E.R., Pelillo, M. (eds.) EMMCVPR 1999. LNCS, vol. 1654, pp. 54–69. Springer, Heidelberg (1999)
Ueda, N., Nakano, R., Ghabramani, Z., Hinton, G.E.: SMEM Algorithm for Mixture Models. Neural Computation 12(9), 2109–2128 (2000)
Biernacki, C., Celeux, G., Govaert, G.: Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models. Computational Statistics & Data Analysis 41(3-4), 561–575 (2003)
Sirovich, L.: Turbulence and the dynamics of coherent structures. Applied Mathematics XLV(3), 561–590 (1987)
Devijver, P., Kittler, J.: Pattern Recognition: A Statistical Approach. Prentice-Hall, Englewood Cliffs (1982)
Troje, N., Bulthoff, H.H.: Face recognition under varying poses: The role of texture and shape. Vision Research 36, 1761–1771 (1996)
Blanz, V., Vetter, T.: A Morphable Model for the Synthesis of 3D Faces. In: SIGGRAPH’99 Conference Proceedings, pp. 187–194 (1999)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Wu, J., Smith, W.A.P., Hancock, E.R. (2007). Learning Mixture Models for Gender Classification Based on Facial Surface Normals. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2007. Lecture Notes in Computer Science, vol 4477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72847-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-540-72847-4_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72846-7
Online ISBN: 978-3-540-72847-4
eBook Packages: Computer ScienceComputer Science (R0)