Gender Classification Using Principal Geodesic Analysis and Gaussian Mixture Models
The aim in this paper is to show how to discriminate gender using a parameterized representation of fields of facial surface normals (needle-maps) which can be extracted from 2D intensity images using shape-from-shading (SFS). We makes use of principle geodesic analysis (PGA) to parameterize the facial needle-maps. Using feature selection, we determine which of the components of the resulting parameter vector are the most significant 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 gender discrimination results that are comparable to human observers.
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- 3.Marr, D.: Vision. W.H. Freeman, San Francisco (1982)Google Scholar
- 4.Smith, W., 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)Google Scholar
- 6.Pennec, X.: Probabilities and statistics on riemannian manifolds: A geometric approach. Technical Report RR-5093, INRIA (2004)Google Scholar
- 7.Jolliffe, I.T.: Principle Component Analysis. Springer, New York (1986)Google Scholar
- 8.Figueiredo, M.A.T., Jain, A.K.: Unsupervised Learning of Finite Mixture Models. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(3) (March 2002)Google Scholar