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Automatic Facial Expression Recognition on A Single 3D Face by Exploring Shape Deformation
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In: http://mmlab.ie.cuhk.edu.hk/archive/2009/Gong_Face_ACM_MM.pdf (2009)
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| 2 |
Automatic facial expression recognition on a single 3d face by exploring shape deformation
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In: http://www-scf.usc.edu/%7Eboqinggo/Paper/Gong_Face_ACM_MM_small.pdf (2009)
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Abstract:
Facial expression recognition has many applications in multime-dia processing and the development of 3D data acquisition tech-niques makes it possible to identify expressions using 3D shape information. In this paper, we propose an automatic facial expres-sion recognition approach based on a single 3D face. The shape of an expressional 3D face is approximated as the sum of two parts, a basic facial shape component (BFSC) and an expressional shape component (ESC). The BFSC represents the basic face structure and neutral-style shape and the ESC contains shape changes caused by facial expressions. To separate the BFSC and ESC, our method firstly builds a reference face for each input 3D non-neutral face by a learning method, which well represents the basic facial shape. Then, based on the BFSC and the original expressional face, a fa-cial expression descriptor is designed. The surface depth changes are considered in the descriptor. Finally, the descriptor is input into an SVM to recognize the expression. Unlike previous meth-ods which recognize a facial expression with the help of manually labeled key points and/or a neutral face, our method works on a sin-gle 3D face without any manual assistance. Extensive experiments are carried out on the BU-3DFE database and comparisons with existing methods are conducted. The experimental results show the effectiveness of our method.
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Keyword:
Categories and Subject Descriptors I.4.7 [Image Processing and Computer Vision; Design; Experimentation; Fea- ture Measurement—Feature representation General Terms Algorithms; I.4.8 [Image Processing and Computer Vision; Scene Analy- sis—Shape
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URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.674.4515
http://www-scf.usc.edu/%7Eboqinggo/Paper/Gong_Face_ACM_MM_small.pdf
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Diverse Sequential Subset Selection for Supervised Video Summarization
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In: http://www-scf.usc.edu/%7Eboqinggo/Paper/nips14_seqdpp.pdf
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| 4 |
Supplementary Material Geodesic Flow Kernel for Unsupervised Domain Adaptation
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In: http://www.cs.utexas.edu/~grauman/papers/subspace-supp-cvpr2012.pdf
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