Abstract
An algorithm for machine recognition of human gender by face images based on machine learning methods is described. The synthesized algorithm consists of two stages, viz. extraction of adaptive features and support vector machine classification. Comparative analysis of operation of the proposed algorithm is performed, and the training and testing technique is given.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Potapov, A.S., Pattern Recognition and Machine Perception: General Approach Based on Principle of Minimal Length of Description, St. Petersburg: Polytekhnika, 2007.
Szeliski, R., Computer Vision: Algorithms and Applications, Springer, 2010.
Zhao, W., Chellappa, R., Phillips, P., and Rosenfeld, A., Face recognition: a literature survey, ACM Computing Surveys (CSUR), 2003, vol. 35, no. 4, pp. 399–458.
Kriegman, D., Yang, M.H., and Ahuja, N., Detecting faces in images: a survey, IEEE Trans. Pattern Analysis Machine Intelligence, 2002, vol. 24, no. 1, pp. 34–58.
Hjelmas, E., Face detection: a Survey, Computer vision and image understanding, 2001, vol. 83, no. 3, pp. 236–274.
Fasel, B. and Luettin, J., Automatic facial expression analysis: a survey, Pattern Recognition Lett., 2003, vol. 36, no. 1, pp. 259–275.
Makinen, E. and Raisamo, R., An experimental comparison of gender classification methods, Pattern Recognition Lett., 2008, vol. 29, no. 10, pp. 1544–1556.
Tamura, S., Kawai, H., and Mitsumoto, H., Male/female identification from 8 to 6 very low resolution face images by neural network, Pattern Recognition Lett., 1996, vol. 29, no. 2, pp. 331–335.
Lyons, M., Budynek, J., Plante, A., and Akamatsu, S. Classifying facial attributes using a 2-d Gabor wavelet representation and discriminant analysis, Proc. Int. Conf. on Automatic Face and Gesture Recognition, 2000, pp. 202–207.
Jain, A. and Huang, J., Integrating independent components and linear discriminant analysis for gender classification, Proc. Int. Conf. on Automatic Face and Gesture Recognition, 2004, pp. 159–163.
Jain, A. and Huang, J., Integrating independent components and linear discriminant analysis for gender classification, Proc. Int. Conf. on Automatic Face and Gesture Recognition, 2004, pp. 159–163.
Saatci, Y. and Town, C., Cascaded classification of gender and facial expression using active appearance models, Proc. Int. Conf. on Automatic Face and Gesture Recognition, 2006, pp. 393–400.
Sun, Z., Bebis, G., Yuan, X., and Louis, S.J., Genetic feature subset selection for gender classification: a comparison study, Proc. IEEE Workshop on Applications of Computer Vision, 2002, pp. 165–170.
Burges, C., A tutorial on support vector machines for pattern recognition, Data Mining Knowledge Discovery, 1998, vol. 2, pp. 121–167.
Sun, N. et al., Gender classification based on boosting local binary pattern, Proc. Int. Symp. on Neural Networks, 2006, vol. 2, pp. 194–201.
Viola, P. and Jones, M., Rapid object detection using a boosted cascade of simple features, Proc. Int. Conf. on Computer Vision and Pattern Recognition, 2001, vol. 1, pp. 511–518.
Gutta, S., Wechsler, H., and Phillips, P.J., Gender and ethnic classification of face images, Proc. Int. Conf. on Automatic Face and Gesture Recognition, 1998, pp. 194–199.
Shmaglit, L.A., Golubev, M.N., and Priorov, A.L., Comparative analysis of algorithms of face detection in images with normal noise, Theses of the 9th All-Russian Sci. Conf. “Neurocomputers and their application,” Moscow, 2011.
Shmaglit, L.A., Golubev, M.N., Ganin, A.N., and Khryashchev, V.V., Gender classification using face image, Proc. of the 14th Int. Conf. “Digital signal processing and its application” (DSPA-2012), Moscow, 2012, vol. 1, pp. 425–428.
Phillips, P.J. et al., The FERET evaluation methodology for face recognition algorithms, IEEE Trans. Pattern Analysis Machine Intelligence, 2000, vol. 22, no. 10, pp. 1090–1104.
Gao, H. and Davis, J., Why direct LDA is not equivalent to LDA, Pattern Recognition Lett., 2006, vol. 39, no. 5, pp. 1002–1006.
Fawcett, T., ROC graphs: Notes and practical considerations for researchers, Pattern Recognition Lett., 2004, vol. 27, no. 8, pp. 882–891.
Author information
Authors and Affiliations
Corresponding author
Additional information
Original Russian Text © V.V. Khryashchev, L.A. Shmaglit, A.L. Priorov, A.M. Shemyakov, 2014, published in Programmirovanie, 2014, Vol. 40, No. 4.
Rights and permissions
About this article
Cite this article
Khryashchev, V.V., Shmaglit, L.A., Priorov, A.L. et al. Extracting adaptive features for gender classification of human face images. Program Comput Soft 40, 215–221 (2014). https://doi.org/10.1134/S0361768814040057
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S0361768814040057