Gender classification by principal component analysis and support vector machine

Authors:
Sunita Kumari;Pankaj Kumar Sa;Banshidhar Majhi
Affiliations:
National Institute of Technology Rourkela, Rourkela, Odisha, India;National Institute of Technology Rourkela, Rourkela, Odisha, India;National Institute of Technology Rourkela, Rourkela, Odisha, India
Venue:
Proceedings of the 2011 International Conference on Communication, Computing & Security
Year:
2011

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Abstract

Performance of any system is identified by its accuracy and speed. Accuracy depends on underlying algorithm while speed depends on size of the database. A tradeoff between these two contradictory aspects has to be achieved. This paper addresses the problem of speed using gender classification. Principal Component AnalysisPCA is used to represent each image as a feature vector in a low dimensional subspace and then a non-linear Support Vector Machine(SVM) is used for gender classification. Experimental results show an accuracy of 92% and is compared with other existing research.