A Component-Based Framework for Generalized Face Alignment

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 s...

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Vydané v:IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Ročník 41; číslo 1; s. 287 - 298
Hlavní autori: Yuchi Huang, Qingshan Liu, Metaxas, D N
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.02.2011
Predmet:
Active shape model
Algorithms
Alignment
Bayes Theorem
Biometric Identification - methods
Bistage statistical model
component detection
Cybernetics
Deformable models
Deformation
Face - anatomy & histology
face alignment
Face detection
Facial animation
Gaussian
Gaussian process latent variable model (GPLVM)
Gaussian processes
Humans
Image Processing, Computer-Assisted - methods
Image reconstruction
Markov Chains
Markov network
Markov random fields
Mathematical models
Mouth
Normal Distribution
Principal Component Analysis
Searching
Shape control
Statistical analysis
Video Recording - methods
ISSN:1083-4419, 1941-0492, 1941-0492
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Popis
Shrnutí: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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ISSN:1083-4419
1941-0492
1941-0492
DOI:10.1109/TSMCB.2010.2052240