Active shape models—their training and application
Computer Vision and Image Understanding
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Is the Set of Images of an Object Under All Possible Illumination Conditions?
International Journal of Computer Vision
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Face Recognition Under Varying Pose
Face Recognition Under Varying Pose
Vectorizing Face Images by Interleaving Shape and Texture Computations
Vectorizing Face Images by Interleaving Shape and Texture Computations
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Journal of Cognitive Neuroscience
Accurate face models from uncalibrated and Ill-Lit video sequences
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Hallucinating face by position-patch
Pattern Recognition
Model-based stereo with occlusions
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
A Comprehensive Survey to Face Hallucination
International Journal of Computer Vision
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This paper presents a new method for face modeling and face recognition from a pair of calibrated stereo cameras. In a first step, the algorithm builds a stereo reconstruction of the face by adjusting the global transformation parameters and the shape parameters of a 3D morphable face model. The adjustment of the parameters is such that stereo correspondence between both images is established, i.e. such that the 3D-vertices of the model project on similarly colored pixels in both images. In a second step, the texture information is extracted from the image pair and represented in the texture space of the morphable face model. The resulting shape and texture coefficients form a person specific feature vector and face recognition is performed by comparing query vectors with stored vectors. To validate our algorithm, an extensive image database was built. It consists of stereo-pairs of 70 subjects. For recognition testing, the subjects were recorded under 6 different head directions, ranging from a frontal to a profile view. The face recognition results are very good, with 100% recognition on frontal views and 97% recognition on half-profile views.