Model-based varying pose face detection and facial feature registration in video images
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Principal Manifolds and Probabilistic Subspaces for Visual Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-based varying pose face detection and facial feature registration in colour images
Pattern Recognition Letters
Recognizing Expressions by Direct Estimation of the Parameters of a Pixel Morphable Model
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Data Driven Image Models through Continuous Joint Alignment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Object Detection with Interleaved Categorization and Segmentation
International Journal of Computer Vision
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We describe a method for modeling object classes (such as faces) using 2D example images and an algorithm for matching a model to a novel image. The object class models are ``learned'''' from example images that we call prototypes. In addition to the images, the pixelwise correspondences between a reference prototype and each of the other prototypes must also be provided. Thus a model consists of a linear combination of prototypical shapes and textures. A stochastic gradient descent algorithm is used to match a model to a novel image by minimizing the error between the model and the novel image. Example models are shown as well as example matches to novel images. The robustness of the matching algorithm is also evaluated. The technique can be used for a number of applications including the computation of correspondence between novel images of a certain known class, object recognition, image synthesis and image compression.