Multi-features fusion based CRFs for face segmentation

doi:10.1109/ICDSP.2015.7252004

	Multi-features fusion based CRFs for face segmentation
	Yin, Yanpeng 1; Zeng, Dan 1; Shen, Wei 1; Cheng, Cheng 2; Zhang, Zhijiang 1
	2015
会议日期	July 21, 2015 - July 24, 2015
会议地点	Singapore, Singapore
DOI	10.1109/ICDSP.2015.7252004
页码	887-891
英文摘要	Face segmentation is quite challenging due to the diversity of hair styles, head poses, clothing, occlusions, and other phenomena. To improve the accuracy of face segmentation from the images with complex scenes, we present a method based on Conditional Random Fields (CRFs) in this paper. The CRFs model is defined on a graph, in which each node corresponds to a superpixel and each edge connects a pair of neighboring superpixels. The features of color and texture are used to define the node energy function, and the position distance and differences of features between adjacent superpixels are used to define the edge energy function. Segmentation is performed by inferring the CRFs model built by fusing node energy function and edge energy function. We evaluate the performance of the proposed method on two unconstrained face databases. Experimental results demonstrate that the proposed method can efficiently partition face images into regions of face, hair, and background. © 2015 IEEE.
会议录	IEEE International Conference on Digital Signal Processing, DSP 2015
语种	英语
内容类型	会议论文
源URL	[http://119.78.100.138/handle/2HOD01W0/4664]
专题	中国科学院重庆绿色智能技术研究院
作者单位	1.Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai, China; 2.Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China
推荐引用方式 GB/T 7714	Yin, Yanpeng,Zeng, Dan,Shen, Wei,et al. Multi-features fusion based CRFs for face segmentation[C]. 见:. Singapore, Singapore. July 21, 2015 - July 24, 2015.