A component-based framework for generalized face alignment

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
Yuchi HuangDimitris N Metaxas

Abstract

This paper presents a component-based deformable model for generalized face alignment, in which a novel bistage statistical model is proposed to account for both local and global shape characteristics. Instead of using statistical analysis on the entire shape, we build separate Gaussian models for shape components to preserve more detailed local shape deformations. In each model of components, a Markov network is integrated to provide simple geometry constraints for our search strategy. In order to make a better description of the nonlinear interrelationships over shape components, the Gaussian process latent variable model is adopted to obtain enough control of shape variations. In addition, we adopt an illumination-robust feature to lead the local fitting of every shape point when light conditions change dramatically. To further boost the accuracy and efficiency of our component-based algorithm, an efficient subwindow search technique is adopted to detect components and to provide better initializations for shape components. Based on this approach, our system can generate accurate shape alignment results not only for images with exaggerated expressions and slight shading variation but also for images with occlusion and heavy ...Continue Reading

References

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Citations

Jul 10, 2014·Neural Networks : the Official Journal of the International Neural Network Society·Risheng LiuZhixun Su
May 2, 2012·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·Jun YuHock Soon Seah
Nov 26, 2015·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Qingshan LiuDacheng Tao

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