Face segmentation using CRFs based on multiple feature fusion

Home > Archive>Volume 38, Issue 6, 2015 >54-59

Face segmentation using CRFs based on multiple feature fusion
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                        Yin Yanpeng1Yin Yanpeng
Key Laboratory of Specialty Fiber Optics and Optical Access Networks, School of Communication and Information Engineering, Shanghai University, Shanghai 200072, China
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Zhou Ying1Zhou Ying
Key Laboratory of Specialty Fiber Optics and Optical Access Networks, School of Communication and Information Engineering, Shanghai University, Shanghai 200072, China
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Zeng Dan1Zeng Dan
Key Laboratory of Specialty Fiber Optics and Optical Access Networks, School of Communication and Information Engineering, Shanghai University, Shanghai 200072, China
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Cheng Cheng2Cheng Cheng
Intelligent Multimedia Technology Research Center, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714,China
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Zhang Zhijiang1Zhang Zhijiang
Key Laboratory of Specialty Fiber Optics and Optical Access Networks, School of Communication and Information Engineering, Shanghai University, Shanghai 200072, China
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Affiliation:1.Key Laboratory of Specialty Fiber Optics and Optical Access Networks, School of Communication and Information Engineering, Shanghai University, Shanghai 200072, China;2.Intelligent Multimedia Technology Research Center, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714,China
Clc Number:TP181
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Abstract:

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(unary) energy function, and the position distance and differences of features between adjacent superpixels are used to define the edge(binary) 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.

Key words:face segmentation; conditional random field; feature extraction

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Online: May 27,2016
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