Bag of features using sparse coding for gender classification

Authors:
Yu Peng;Suhuai Luo;Min Xu;Zefeng Ni;Jesse S. Jin;Jinqiao Wang;Gang Zhao
Affiliations:
University of Newcastle, Australia;University of Newcastle, Australia;FEIT, University of Technology, Sydney, Australia and National Laboratory of Pattern Recognition, Institute of Automation, China;FEIT, University of Technology, Sydney, Australia;FEIT, University of Technology, Sydney, Australia;National Laboratory of Pattern Recognition, Institute of Automation, China;Central China Normal University, China
Venue:
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
Year:
2012

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Abstract

Gender classification is challenging. Methods for gender classification need to discriminate subtle differences between male and female. Bag-of-Features (BoF) method with sparse coding has been proven very powerful in image classification. In this paper, we apply BoF method for gender classification. We use two sets of images: training images and testing images. All images are represented by a set of Scale-Invariant Feature Transform (SIFT) descriptors. In training stage, using sparse coding, Visual Words Dictionary (VWD) is constructed from SIFT descriptors extracted from training images. In testing, SIFT descriptors of testing images are approximated by visual words in VWD. The choices of approximating visual words determine the classification decision. We apply our method and another two popular methods on public dataset for gender classification. We achieved promising results.