Abstract
In many social interactions, it is important to correctly recognize the gender. Researches have addressed this issue based on facial images, ear images and gait. In this paper, we present an approach for gender classification using facial images based upon sparse representation and Basis Pursuit. In sparse representation, the training data is used to develop a dictionary based on extracted features. Classification is achieved by representing the extracted features of the test data using the dictionary. For this purpose, basis pursuit is used to find the best representation by minimizing the l 1 norm. In this work, Gabor filters are used for feature extraction. Experimental results are conducted on the FERET data set and obtained results are compared with other works in this area. The results show improvement in gender classification over existing methods.
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References
Aharon, M., Elad, M., Bruckstein, A.: K-svd: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing 54(11), 4311–4322 (2006)
Baraniuk, R., Candes, E., Elad, M., Ma, Y.: Applications of sparse representation and compressive sensing. Proceedings of the IEEE 98(6), 906–909 (2010)
Cao, L., Dikmen, M., Fu, Y., Huang, T.S.: Gender recognition from body. In: Proceedings of the 16th ACM International Conference on Multimedia, MM 2008, New York, NY, USA, pp. 725–728 (2008)
Chen, D.-Y., Lin, K.-Y.: Robust gender recognition for real-time surveillance system. In: IEEE International Conference on Multimedia and Expo (ICME), pp. 191–196 (July 2010)
Chen, S.S., Donoho, D.L., Michael, Saunders, A.: Atomic decomposition by basis pursuit. SIAM Journal on Scientific Computing 20, 33–61 (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 3, NIPS-3, San Francisco, CA, USA, pp. 564–571 (1990)
Donoho, D.L.: Compressed sensing. IEEE Transaction on Information Theory 52(4) (2006)
Golomb, B.A., Lawrence, D.T., Sejnowski, T.J.: Sexnet: A neural network identifies sex from human faces. In: Proceedings Conf. Advances in Neural Information Processing Systems 3, pp. 572–577 (1990)
Gutta, S., Wechsler, H.: Gender and ethnic classification of human faces using hybrid classifiers. In: International Joint Conference on Neural Networks, IJCNN 1999, vol. 6, pp. 4084–4089 (1999)
Harb, H., Chen, L.: Gender identification using a general audio classifier. In: Proceedings of the International Conference on Multimedia and Expo, ICME 2003, Washington, DC, USA, pp. 733–736 (2003)
Hsu, R.-L., Abdel-Mottaleb, M., Jain, A.: Face detection in color images. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5), 696–706 (2002)
Jain, A., Huang, J., Fang, S.: Gender identification using frontal facial images. In: IEEE International Conference on Multimedia and Expo, ICME 2005, p. 4 (July 2005)
Liu, C., Wechsler, H.: Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Transactions on Image Processing 11(4), 467–476 (2002)
Llagostera Casanovas, A., Monaci, G., Vandergheynst, P., Gribonval, R.: Blind audiovisual source separation based on sparse redundant representations. IEEE Transactions on Multimedia 12(5), 358–371 (2010)
Lu, H., Huang, Y., Chen, Y., Yang, D.: Automatic gender recognition based on pixel-pattern-based texture feature. Journal of Real-Time Image Processing 3, 109–116 (2008)
Moghaddam, B., Yang, M.-H.: Gender classification with support vector machines. In: Fourth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 306–311 (2000)
Patel, V.M., Wu, T., Biswas, S., Phillips, P.J., Chellappa, R.: Dictionary-based face recognition under variable lighting and pose. IEEE Transactions on Information Forensics and Security 7(3), 954–965 (2012)
Phillips, P., Moon, H., Rizvi, S., Rauss, P.: The feret evaluation methodology for face-recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10), 1090–1104 (2000)
Protter, M., Elad, M.: Image sequence denoising via sparse and redundant representations. IEEE Transactions on Image Processing 18(1), 27–35 (2009)
Wright, J., Yang, A., Ganesh, A., Sastry, S., Ma, Y.: Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(2), 210–227 (2009)
Wu, B., Ai, H., Huang, C.: Facial image retrieval based on demographic classification. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 3, pp. 914–917 (2004)
Yang, M., Zhang, L.: Gabor feature based sparse representation for face recognition with gabor occlusion dictionary. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part VI. LNCS, vol. 6316, pp. 448–461. Springer, Heidelberg (2010)
Yu, S., Tan, T., Huang, K., Jia, K., Wu, X.: A study on gait-based gender classification. IEEE Transactions on Image Processing 18(8), 1905–1910 (2009)
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Khorsandi, R., Abdel-Mottaleb, M. (2013). Gender Classification Using Facial Images and Basis Pursuit. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8047. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40261-6_35
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DOI: https://doi.org/10.1007/978-3-642-40261-6_35
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