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	Gender classification of full body images based on the convolutional neural network
	Yu, Zhenxia1; Shen, Chengxuan1; Chen, Lin2
	2018
摘要	Gender classification is one of the most interesting and challenging problems in computer vision and has been widely studied based on facial images. However, the images of human we taken from the real-world surveillance are mostly full body and relatively blurry, which is much more difficult to classify due to different poses and backgrounds in unconstrained scenarios. In this paper, we propose a new network structure based on a convolutional neural network (CNN), which is less complicated and has a small number of layers. Moreover, it can achieve a high accuracy with even trained with limited data. We evaluate our method on the dataset collected from real-world video surveillance and compare various learning algorithms including Alex Net and Google Net. The experimental results showed that the proposed model achieved better results than the tested state-of-the-art network structures. © 2017 IEEE.
语种	英语
DOI	10.1109/SPAC.2017.8304366
会议(录)名称	2017 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2017
页码	707-711
收录类别	EI
会议地点	Shenzhen, China
会议日期	December 15, 2017 - December 17, 2017