Linear Object Classes and Image Synthesis From a Single Example Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multidimensional Morphable Models: A Framework for Representing and Matching Object Classes
International Journal of Computer Vision
Model-based varying pose face detection and facial feature registration in video images
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Model-based varying pose face detection and facial feature registration in colour images
Pattern Recognition Letters
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active morphable model: an efficient method for face analysis
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Parametric stereo for multi-pose face recognition and 3d-face modeling
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
Synthesis of a face image at a desired pose from a given pose
Pattern Recognition Letters
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The correspondence problem in computer vision is basically a matching task between two or more sets of features. In this paper, we introduce a vectorized image representation, which is a feature-based representation where correspondence has been established with respect to a reference image. This representation has two components: (1) shape, or (x, y) feature locations, and (2) texture, defined as the image grey levels mapped onto the standard reference image. This paper explores an automatic technique for ``vectorizing'''' face images. Our face vectorizer alternates back and forth between computation steps for shape and texture, and a key idea is to structure the two computations so that each one uses the output of the other. A hierarchical coarse-to-fine implementation is discussed, and applications are presented to the problems of facial feature detection and registration of two arbitrary faces.