Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
A probabilistic model for face transformation with application to person identification
EURASIP Journal on Applied Signal Processing
Frontal face authentication using discriminating grids withmorphological feature vectors
IEEE Transactions on Multimedia
Face authentication with Gabor information on deformable graphs
IEEE Transactions on Image Processing
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A novel probabilistic deformable model of face mapping was recently introduced and successfully applied to automatic person identification. In this paper, we consider the use of discrimination to improve the performance of this system. It is possible to introduce discriminative information at two different levels: 1) in the face representations and 2) in the deformable model used to match face images. We explore both types of discrimination and compare them in terms of performance and computational complexity. Results are presented on the FERET face database for a face identification task and show that, in this framework and for the discriminative techniques that were considered, the discrimination of the deformable model should be preferred and can result in a 25-40% relative error rate reduction compared to the baseline system.