A Framework for Weighted Fusion of Multiple Statistical Models of Shape and Appearance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning AAM fitting through simulation
Pattern Recognition
A comparative study of facial appearance modeling methods for active appearance models
Pattern Recognition Letters
Image and Vision Computing
On color texture normalization for active appearance models
IEEE Transactions on Image Processing
Office-mate: selective attention and incremental object perception
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Video-based face model fitting using Adaptive Active Appearance Model
Image and Vision Computing
A review of active appearance models
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Real-Time Facial Feature Tracking on a Mobile Device
International Journal of Computer Vision
Hi-index | 0.01 |
The active appearance model (AAM) is a powerful tool for modeling images of deformable objects and has been successfully used in a variety of alignment, tracking, and recognition applications. AAM uses subspace-based deformable models to represent the images of a certain object class. In general, fitting such complicated models to previously unseen images using standard optimization techniques is a computationally complex task because the gradient matrix has to be numerically computed at every iteration. The critical feature of AAM is a fast convergence scheme which assumes that the gradient matrix is fixed around the optimal coefficients for all images. Our work in this paper starts with the observation that such a fixed gradient matrix inevitably specializes to a certain region in the texture space, and the fixed gradient matrix is not a good estimate of the actual gradient as the target texture moves away from this region. Hence, we propose an adaptive AAM algorithm that linearly adapts the gradient matrix according to the composition of the target image's texture to obtain a better estimate for the actual gradient. We show that the adaptive AAM significantly outperforms the basic AAM, especially in images that are particularly challenging for the basic algorithm. In terms of speed and accuracy, the idea of a linearly adaptive gradient matrix presented in this paper provides an interesting compromise between a standard optimization technique that recomputes the gradient at every iteration and the fixed gradient matrix approach of the basic AAM.