Abstract
This paper proposes a novel framework for dealing with multi-view gender classification problems and shows its feasibility on the CAS-PEAL database of face images. The framework consists of three stages. First, wavelet transform is used to intensify multi-scale edges and remove effects of illumination and noises. Second, instead of traditional Euclidean distance, image Euclidean distance which considers the spatial relationships between pixels is used to measure the distance between images. Last, a two layer support vector machine is proposed, which divides face images into different poses in the first layer, and then recognizes the gender with different support vector machines in the second layer. Compared with traditional support vector machines and min-max modular network with support vector machines, our method achieves higher classification accuracy and spends less training and test time.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Gutta, S., Huang, J., Jonathon, P., Wechsler, H.: Mixture of experts for classification of gender, ethnic origin, andpose of human faces. IEEE Transactions on Neural Networks 11(4), 948–960 (2000)
Moghaddam, B., Yang, M.H.: Learning gender with support faces. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5), 707–711 (2002)
Khan, A.: Combination and optimization of classifiers in gender classification using genetic programming. International Journal of Knowledge-Based and Intelligent Engineering Systems 9(1), 1–11 (2005)
Lian, H.C., Lu, B.L., Takikawa, E., Hosoi, S.: Gender recognition using a min-max modular support vector machine. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3611, pp. 438–441. Springer, Heidelberg (2005)
Lian, H.C., Lu, B.L.: Multi-view gender classification using local binary patterns and support vector machines. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3972, pp. 202–209. Springer, Heidelberg (2006)
Luo, J., Lu, B.L.: Gender recognition using a min-max modular support vector machine with equal clustering. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3972, pp. 210–215. Springer, Heidelberg (2006)
Kim, H.C., Kim, D., Ghahramani, Z., Bang, S.Y.: Appearance-based gender classification with Gaussian processes. Pattern Recognition Letters 27(6), 618–626 (2006)
Balci, K., Atalay, V.: PCA for gender estimation: Which eigenvectors contribute. Proceedings of Sixteenth International Conference on Pattern Recognition 3, 363–366 (2002)
Jain, A., Huang, J.: Integrating independent components and linear discriminant analysis for gender classification. In: Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 159–163 (2004)
OToole, A.J., Deffenbacher, K.A., Valentin, D., McKee, K., Huff, D., Abdi, H.: The perception of face gender: The role of stimulus structure in recognition and classification. Memory and Cognition 26(1), 146–160 (1998)
Cottrell, G.W., Metcalfe, J.: EMPATH: face, emotion, and gender recognition using holons. In: Proceedings of the 1990 conference on Advances in neural information processing systems, pp. 564–571 (1990)
Edelman, B., Valentin, D., Abdi, H.: Sex classification of face areas: how well can a linear neural network predict human performance. Journal of Biological System 6(3), 241–264 (1998)
Golomb, B., Lawrence, D., Sejnowski, T.: SexNet: A neural network identifies sex from human faces. In: Proceedings of the 1990 conference on Advances in neural information processing, pp. 572–577 (1990)
Gao, W., Cao, B., Shan, S., Zhou, D., Zhang, X., Zhao, D.: The CAS-PEAL large-scale Chinese face database and baseline evaluations. Technical report of JDL (2004), http://www.jdl.ac.cn/peal/pealtr.pdf
Wang, L., Zhang, Y., Feng, J.: On the Euclidean distance of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(8), 1334–1339 (2005)
Mallat, S.: Multifrequency channel decompositions of images and wavelet models. IEEE Transactions on Acoustics, Speech, and Signal Processing 37(12), 2091–2110 (1989)
Cohen, A., Kovacevic, J.: Wavelets: the mathematical background. Proceedings of the IEEE 84(4), 514–522 (1996)
Mallat, S., Zhong, S.: Characterization of signals from multiscale edges. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(7), 710–732 (1992)
Vapnik, V.N.: Statistical learning theory. Wiley, New York (1998)
Platt, J.C.: Fast training of support vector machines using sequential minimal optimization. Advances in kernel methods: support vector learning, 185–208 (1999)
Huang, J., Shao, X., Wechsler, H.: Face pose discrimination using support vector machines (SVM). In: Fourteenth International Conference on Pattern Recognition, vol. 1, pp. 154–156 (1998)
Lu, B.L., Wang, K.A., Utiyama, M., Isahara, H.: A part-versus-part method for massively parallel training of support vector machines. In: 2004 IEEE International Joint Conference on Neural Networks, vol. 1, pp. 735–740 (2004)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, J., Lu, BL. (2008). A Framework for Multi-view Gender Classification. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4984. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69158-7_100
Download citation
DOI: https://doi.org/10.1007/978-3-540-69158-7_100
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69154-9
Online ISBN: 978-3-540-69158-7
eBook Packages: Computer ScienceComputer Science (R0)