Abstract
Gender recognition using facial images plays an important role in biometric technology. Multiscale texture descriptors perform better in gender recognition because they encode the multiscale facial microstructures in a better way. We present a gender recognition system that uses SVM, two-stage feature selection and multiscale texture feature based on Nonsubsampled Contourlet Transform and Weber law descriptor (NSCT-WLD). The proposed system has better recognition rate (99.50%) than the state-of-the-art methods on FERET database. This research also reveals that in NSCT decomposition what is essential for face recognition and what is important for other tasks like age detection.
Chapter PDF
References
Zang, J., Lu, B.L.: A support vector machine classifier with automatic confidence and its application to gender classification. Neurocomputing 74, 1926–1935 (2011)
Moghaddam, B., Yang, M.-H.: Gender classification with support vector machines. In: Proc. IEEE International Conference on Automatic Face and Gesture Recognition, pp. 306–311 (March 2000)
Gutta, S., Wechsler, H., Phillips, P.: Gender and ethnic classification of face images. In: Third IEEE International Conference on Automatic Face and Gesture Recognition (FG 1998), pp. 194–199 (1998)
Ullah, I., Hussain, M., Aboalsamh, H., Muhammad, G., Mirza, A.M., Bebis, G.: Gender Recognition from Face Images with Dyadic Wavelet Transform and Local Binary Pattern. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Fowlkes, C., Wang, S., Choi, M.-H., Mantler, S., Schulze, J., Acevedo, D., Mueller, K., Papka, M. (eds.) ISVC 2012, Part II. LNCS, vol. 7432, pp. 409–419. Springer, Heidelberg (2012)
Baluja, S., Rowley, H.: Boosting sex identification performance. International Journal of Computer Vision 71(1), 111–119 (2007)
Lu, L., Shi, P.: Fusion of multiple facial regions for expression-invariant gender classification. IEICE Electronic Express 6(10), 587–593 (2009)
Alexandre, L.A.: Gender recognition: A multiscale decision fusion approach. Pattern Recognition Letters 31, 1422–1427 (2010)
Zhou, J., Cunha, A.L., Do, M.N.: Nonsubsampledcontourlet transform: construction and application in enhancement. In: Proc. ICIP 2005, pp. I 469-72 (2005)
Chen, J., Shan, S., He, C., Zhao, G., Pietikainen, M., Chen, X., Gao, W.: WLD: A robust local image descriptor. IEEE TPAMI 32(9), 1705–1720 (2010)
Hart, P.E., Duda, R.O., Stork, D.G.: Pattern Classification. Wiley-Interscience Publication (2001)
Sun, Y., Todorovic, S., Goodison, S.: Local-learning-based feature selection for high-dimensional data analysis. IEEE TPAMI 32(9), 1610–1626 (2010)
Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET evaluation methodology for face-recognition algorithms. IEEE TPAMI 22(10), 1090–1104 (2000)
Veropoulos, K., Bebis, G., Webster, M.A.: Investigating the impact of face cate-gorization on recognition performance. In: Bebis, G., Boyle, R., Koracin, D., Parvin, B. (eds.) ISVC 2005. LNCS, vol. 3804, pp. 207–218. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hussain, M., Al-Otaibi, S., Muhammad, G., Aboalsamh, H., Bebis, G., Mirza, A.M. (2013). Gender Recognition Using Nonsubsampled Contourlet Transform and WLD Descriptor. In: Kämäräinen, JK., Koskela, M. (eds) Image Analysis. SCIA 2013. Lecture Notes in Computer Science, vol 7944. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38886-6_36
Download citation
DOI: https://doi.org/10.1007/978-3-642-38886-6_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38885-9
Online ISBN: 978-3-642-38886-6
eBook Packages: Computer ScienceComputer Science (R0)