Active shape models—their training and application
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
A bootstrapping algorithm for learning linear models of object classes
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
Active Appearance Models Revisited
International Journal of Computer Vision
Automatic Construction of Active Appearance Models as an Image Coding Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Data Driven Image Models through Continuous Joint Alignment
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust autonomous model learning from 2D and 3D data sets
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Learning a restricted Bayesian network for object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Lucas-Kanade based entropy congealing for joint face alignment
Image and Vision Computing
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It is a challenging vision problem to discover non-rigid shape deformation for an image ensemble belonging to a single object class, in an automatic or semi-supervised fashion. The conventional semi-supervised approach [1] uses a congealing-like process to propagate manual landmark labels from a few images to a large ensemble. Although effective on an inter-person database with a large population, there is potential for increased labeling accuracy. With the goal of providing highly accurate labels, in this paper we present a parametric curve representation for each of the seven major facial contours. The appearance information along the curve, named curve descriptor, is extracted and used for congealing. Furthermore, we demonstrate that advanced features such as Histogram of Oriented Gradient (HOG) can be utilized in the proposed congealing framework, which operates in a dual-curve congealing manner for the case of a closed contour. With extensive experiments on a 300-image ensemble that exhibits moderate variation in facial pose and shape, we show that substantial progress has been achieved in the labeling accuracy compared to the previous state-of-the-art approach.