Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Interpretation and Coding of Face Images Using Flexible Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Face Identification by Fitting a 3D Morphable Model Using Linear Shape and Texture Error Functions
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
A Non-Linear Gray-Level Appearance Model Improves Active Shape Model Segmentation
MMBIA '01 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA'01)
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ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
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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, 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 nonfrontal face alignment task demonstrate the effectiveness and robustness of the proposed E-TDSM algorithm.