Abstract
Gender classification algorithms uses a number of dimensionality reduction techniques. These techniques are used to remove linear and non–linear dependency among the data. Most popular techniques are Principal Component Analysis (PCA) and Independent Component analysis (ICA). PCA makes the data uncorrelated while ICA gives independent data. In this approach, infomax ICA has been used for gender recognition and it is outperforming PCA. To perform classification using ICA features, Support Vector Machine (SVM) has been used. For larger training set SVM is performing with an accuracy of 98%. The accuracy values are obtained for change in size of testing set and the proposed system performs with an average accuracy of 96%.
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References
Baek, K., Draper, B.A., Beveridge, J.R., She, K.: Pca vs. ica: A comparison on the feret data set. In: Joint Conference on Information Sciences, pp. 824–827 (2002)
Bartlett, M., Movellan, J., Sejnowski, T.: Face recognition by independent component analysis. IEEE Transactions on Neural Networks 13(6), 1450–1464 (2002)
Bell, A., Sejnowski, T.J.: The ‘independent components’ of natural scenes are edge filters. Vision Research 37, 3327–3338 (1997)
Bell, A.J., Sejnowski, T.J.: An information-maximization approach to blind separation and blind deconvolution. Neural Computation 7, 1129–1159 (1995)
Jain, A.K., Maltoni, D., Maio, D., Prabhakar, S.: Guide to Biometrics. Springer, Heidelberg (2003)
Hyvrinen, A., Oja, E.: Independent component analysis: algorithms and applications. Neural Networks 13, 411–430 (2000)
Jain, A., Huang, J.: Integrating independent components and support vector machines for gender classification. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 3, pp. 558–561 (August 2004)
Liu, C., Wechsler, H.: Comparative assessment of independent component analysis (ica) for face recognition. In: International Conference on Audio and Video Based Biometric Person Authentication, pp. 22–24 (1999)
Moghaddam, B.: Principal manifolds and Bayesian subspaces for visual recognition. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1131–1136 (1999)
Moghaddam, B., Yang, M.-H.: Learning gender with support faces. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5), 707–711 (2002)
Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The feret evaluation methodology for face recognition algorithms. In: In (a) Example 1 (b) Example 1 (c) Right ear) (d) Mirroed Left ear
Sergios Theodoridis, K.K.: Pattern Recognition. Springer, Heidelberg (2006)
Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cognitive Neuroscience 3(1), 71–86 (1991)
Yuen, P.C., Lai, J.H.: Independent component analysis of face images. In: IEEE Workshop on Biologically Motivated Computer Vision (2000)
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Kumari, S., Majhi, B. (2011). Classifying Gender from Faces Using Independent Components. In: Nagamalai, D., Renault, E., Dhanuskodi, M. (eds) Trends in Computer Science, Engineering and Information Technology. CCSEIT 2011. Communications in Computer and Information Science, vol 204. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24043-0_60
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DOI: https://doi.org/10.1007/978-3-642-24043-0_60
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