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 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.
Huang
Yuchi
Y
Department of Computer Science, Rutgers University, New Brunswick, NJ 08854, USA. yuchuang@cs.rutgers.edu
Liu
Qingshan
Q
Metaxas
Dimitris N
DN
D000465:Algorithms; D001499:Bayes Theorem; D056667:Biometric Identification / Q000379:methods*; D005145:Face / Q000033:anatomy & histology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008390:Markov Chains; D016011:Normal Distribution; D025341:Principal Component Analysis; D014741:Video Recording / Q000379:methods*