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 rectan...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 face alignment task demonstrate the effectiveness and robustness EM approach. The extensive experiments on non-frontal of the proposed E-TDSM algorithm.展开更多
文摘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 face alignment task demonstrate the effectiveness and robustness EM approach. The extensive experiments on non-frontal of the proposed E-TDSM algorithm.
