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Robust Non-Frontal Face Alignment with Edge Based Texture

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Abstract

This paper proposes a new algorithm, called Edge-based Texture Driven Shape Model (E-TDSM), for non-frontal 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, Expectation-Maximization (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.

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Authors and Affiliations

  1. LMAM, School of Mathematical Sciences, Peking University, Beijing, 100871, P.R. China

    Hua Li & Li-Zhong Peng

  2. Department of Information Engineering, The Chinese University of Hong Kong Hong Kong Special Administration Region, P.R. China

    Shui-Cheng Yan

Authors
  1. Hua Li
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  2. Shui-Cheng Yan
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  3. Li-Zhong Peng
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Corresponding author

Correspondence to Hua Li.

Additional information

Supported by the National Natural Science Foundation of China under Grant No. 10471002 and the National Basic Research 973 Program of China under Grant No. G1999075105.

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Li, H., Yan, SC. & Peng, LZ. Robust Non-Frontal Face Alignment with Edge Based Texture. J Comput Sci Technol 20, 849–854 (2005). https://doi.org/10.1007/s11390-005-0849-8

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  • DOI: https://doi.org/10.1007/s11390-005-0849-8

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