Abstract
In this paper we study the problem of gender recognition from human body. To represent human body images for the purpose of gender recognition, we propose to use the biologically-inspired features in combination with manifold learning techniques. A framework is also proposed to deal with the body pose change or view difference in gender classification. Various manifold learning techniques are applied to the bio-inspired features and evaluated to show their performance in different cases. As a result, different manifold learning methods are used for different tasks, such as the body view classification and gender classification at different views. Based on the new representation and classification framework, a gender recognition accuracy of about 80% can be obtained on a public available pedestrian database.
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Guo, G., Mu, G., Fu, Y. (2010). Gender from Body: A Biologically-Inspired Approach with Manifold Learning. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12297-2_23
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DOI: https://doi.org/10.1007/978-3-642-12297-2_23
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