Robust Active Shape Model Using AdaBoosted Histogram Classifiers and Shape Parameter Optimization

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 shadows, which are rarely reported in previous work.

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Robust Active Shape Model Using AdaBoosted Histogram Classifiers and Shape Parameter Optimization

Cited by 3 publications

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Detection and amendment of shape distortions based on moment invariants for active shape models

Detection and amendment of shape distortions based on moment invariants for active shape models

On-line Boosting and Vision

A Component-Based Framework for Generalized Face Alignment

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