IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Model-Based Motion Estimation
ECCV '92 Proceedings of the Second European Conference on Computer Vision
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Interpreting Face Images Using Active Appearance Models
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
Active Appearance Models Revisited
International Journal of Computer Vision
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Generic vs. person specific active appearance models
Image and Vision Computing
The Asymmetry of Image Registration and Its Application to Face Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Hallucination: Theory and Practice
International Journal of Computer Vision
Automatic Fitting of a Deformable Face Mask Using a Single Image
MIRAGE '09 Proceedings of the 4th International Conference on Computer Vision/Computer Graphics CollaborationTechniques
A review of active appearance models
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Frontal face generation from multiple low-resolution non-frontal faces for face recognition
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Hi-index | 0.00 |
Active Appearance Models (AAM) are compact representations of the shape and appearance of objects. Fitting AAMs to images is a difficult, non-linear optimization task. Traditional approaches minimize the L2 norm error between the model instance and the input image warped onto the model coordinate frame. While this works well for high resolution data, the fitting accuracy degrades quickly at lower resolutions. In this paper, we show that a careful design of the fitting criterion can overcome many of the low resolution challenges. In our resolution-aware formulation (RAF), we explicitly account for the finite size sensing elements of digital cameras, and simultaneously model the processes of object appearance variation, geometric deformation, and image formation. As such, our Gauss-Newton gradient descent algorithm not only synthesizes model instances as a function of estimated parameters, but also simulates the formation of low resolution images in a digital camera. We compare the RAF algorithm against a state-of-the-art tracker across a variety of resolution and model complexity levels. Experimental results show that RAF considerably improves the estimation accuracy of both shape and appearance parameters when fitting to low resolution data.