Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
International Journal of Computer Vision
Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Eye Center Localization Using Adaptive Templates
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 5 - Volume 05
Learn Discriminant Features for Multi-View Face and Eye Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Regression and Classification Approaches to Eye Localization in Face Images
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Generic vs. person specific active appearance models
Image and Vision Computing
A Viewpoint Invariant, Sparsely Registered, Patch Based, Face Verifier
International Journal of Computer Vision
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The accurate alignment of faces is essential to almost all automatic tasks involving face analysis. A common paradigm employed for this task is to exhaustively evaluate a face template/classifier across a discrete set of alignments (typically translation and scale). This strategy, provided the template/classifier has been trained appropriately, can give one a reliable but "rough" estimate of where the face is actually located. However, this estimate is often too poor to be of use in most face analysis applications (e.g. face recognition, audio-visual speech recognition, expression recognition, etc.). In this paper we present an approach that is able to refine this initial rough alignment using a gradient descent approach, so as to gain adequate alignment. Specifically, we propose an efficient algorithm which we refer to as the sequential algorithm, which is able to obtain a good balance between alignment accuracy and computational efficiency. Experiments are conducted on frontal and non-frontal faces.