We propose a robust face alignment algorithm with a novel discriminative local texture model. Different from the conventional descriptive PCA local texture model in ASM, classifiers using LUT-type Haar-like features are trained from a large data set as local texture model. The strong discriminative power of the classifier greatly improves the accuracy and robustness of local searching on faces with expression variation and ambiguous contours. A Bayesian framework is configured for shape parameter optimization and the algorithm is implemented in a hierarchical structure for efficiency. Extensive experiments are reported to show its accuracy and robustness.


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    Title :

    Robust face alignment based on local texture classifiers


    Contributors:
    Li Zhang, (author) / Haizhou Ai, (author) / Shengjun Xin, (author) / Chang Huang, (author) / Shuichiro Tsukiji, (author) / Shihong Lao, (author)


    Publication date :

    2005-01-01


    Size :

    453521 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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