Abstract
In the paper, we propose a Bayesian classifier which exploits non-parametric model to identify the gender from the facial images. Our major contribution is that we use feature patch-based non-parametric method to generate the posteriori of male and female based on the characteristics of the labeled training image patches. Our system consists of four modules. First, we use AAM model to identify facial feature points. Facial images are represented by the overlapping feature patches around the feature points. Second, from the labeled training patches, we select a smaller subset as the patch library based on the K means clustering. Third, in training, we embed the gender characteristics of the training feature patches as the posteriori of the library patches. Fourth, in testing, we integrate the posterior of the test patches to determine the gender. The experimental results demonstrate that our proposed method is better than the conventional non-feature-patch-based methods.
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Saatci, Y., Town, C.: Cascaded Classification of Gender and Facial Expression Using Active Appearance Models. In: AFGR 2006, pp. 393–400 (April 2006)
Mäkinen, E., Raisamo, R.: Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces. IEEE Trans. on PAMI 30(3), 541–547 (2008)
Lian, H.C., Lu, B.L.: Multi-View Gender Classification Using Multi-Resolution Local Binary Patterns and Support Vector Machines. Int. J. of Neural Systems 17(6) (2007)
Xia, B., Sun, H., Lu, B.L.: Multi-View Gender Classification Based on Local Gabor Binary Mapping Pattern and Support Vector Machines. In: IEEE ICNN, p. 3388 (2008)
Fang, Y., Wang, Z.: Improving LBP Features For Gender Classification. In: Int. Conf. on Wavelet Analysis and Pattern Recognition, pp. 373–377 (August 2008)
Li, J., Lu, B.-L.: A Framework for Multi-view Gender Classification. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds.) ICONIP 2007, Part I. LNCS, vol. 4984, pp. 973–982. Springer, Heidelberg (2008)
Leng, X.M., Wang, Y.D.: Gender Classification Based on Fuzzy SVM. In: Int. Conf. on Machine Learning and Cybernetics (2008)
Baluja, S., et al.: Boosting Sex Identification Performance. Int. J. of Computer Vision 71(1), 111–119 (2007)
Shen, B.-C., Chen, C.S., Hsu, H.H.: Fast Gender Recognition by Using a Shared Integral Image Approach. In: IEEE ICASSP (2009)
Wang, Z.-H., Mu, Z.-C.: Gender Classification Using Selected Independent-Features Based on Genetic Algorithm. In: 8th Int. Conf. on Machine Learning and Cybernetics, pp. 394–398 (July 2009)
Andreu, Y., Mollineda, R.A.: On the Complementarity of Face Parts for Gender Recognition. In: Ruiz-Shulcloper, J., Kropatsch, W.G. (eds.) CIARP 2008. LNCS, vol. 5197, pp. 252–260. Springer, Heidelberg (2008)
Toews, M., Arbel, T.: Detection, Localization, and Sex Classification of Faces from Arbitrary Viewpoints and under Occlusion. IEEE Trans. on PAMI 31(9) (September 2009)
Aghajanian, J., et al.: Within-Object Classification. In: IEEE 12th ICCV (2009)
Li, Z., Zhou, X., Huang, T.S.: Spatial Gaussian Mixture Model for Gender Recognition. In: IEEE ICIP (November 2009)
Cootes, T.F., Wheeler, G.V.: View-Based Active Appearance Models. Image and Vision Computing 20, 657–664 (2002)
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Lin, SJ., Huang, CL., Hsu, SC. (2011). Gender Identification Using Feature Patch-Based Bayesian Classifier. In: Ho, YS. (eds) Advances in Image and Video Technology. PSIVT 2011. Lecture Notes in Computer Science, vol 7088. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25346-1_11
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DOI: https://doi.org/10.1007/978-3-642-25346-1_11
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