首页|Robust Non-Frontal Face Alignment with Edge Based Texture
Robust Non-Frontal Face Alignment with Edge Based Texture
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NETL
NSTL
万方数据
维普
This paper proposes a new algorithm, called Edge-based Texture Driven Shape Model (E-TDSM), for nonfrontal face alignment task. First, the texture is defined as the un-warped edge image contained in the shape rectangle; then,a Bayesian network is constructed to describe the relationship between the shape and texture models; finally, ExpectationMaximization (EM) approach is utilized to infer the optimal texture and position parameters from the observed shape and texture information. Compared with the traditional shape localization algorithms, E-TDSM has the following advantages:1) the un-warped edge-based texture can better predict the shape and is more robust to the illumination and expression variation than the conventional warped gray-level based texture; 2) the presented Bayesian network indicates the logic structure of the face alignment task; and 3) the mutually enhanced shape and texture observations are integrated to infer the optimal parameters of the proposed Bayesian network using EM approach. The extensive experiments on non-frontal face alignment task demonstrate the effectiveness and robustness of the proposed E-TDSM algorithm.
computer visionface and gesture recognitionfeature evaluation and selectionBayesian network
Hua Li、Shui-Cheng Yan、Li-Zhong Peng
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LMAM, School of Mathematical Sciences, Peking University, Beijing 100871, P.R. China
Department of Information Engineering, The Chinese University of Hong Kong Hong Kong Special Administration Region, P.R. China
NETL
NSTL
万方数据
维普
