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	Gender and Smile Classification using Deep Convolutional Neural Networks
	Kaipeng Zhang; Lianzhi Tan; Zhifeng Li; Yu Qiao
	2016
会议名称	CVPR Workshop 2016
会议地点	美国
英文摘要	Facial gender and smile classification in unconstrained environment is challenging due to the invertible and large variations of face images. In this paper, we propose a deep model composed of GNet and SNet for these two tasks. We leverage the multi-task learning and the general-to-specific fine-tuning scheme to enhance the performance of our model. Our strategies exploit the inherent correlation between face identity, smile, gender and other face attributes to relieve the problem of over-fitting on small training set and improve the classification performance. We also propose the tasks-aware face cropping scheme to extract attribute- specific regions. The experimental results on the ChaLearn 16 FotW dataset for gender and smile classification demonstrate the effectiveness of our proposed methods.
收录类别	EI
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/10028]
专题	深圳先进技术研究院_集成所
作者单位	2016
推荐引用方式 GB/T 7714	Kaipeng Zhang,Lianzhi Tan,Zhifeng Li,et al. Gender and Smile Classification using Deep Convolutional Neural Networks[C]. 见:CVPR Workshop 2016. 美国.

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