Probabilistic linear discriminant analysis for inferences about identity.
2007 IEEE 11TH INTERNATIONAL CONFERENCE ON COMPUTER VISION, VOLS 1-6.
(pp. 1751 - 1758).
Many current face recognition algorithms perform badly when the lighting or pose of the probe and gallery images differ In this paper we present a novel algorithm designed for these conditions. We describe face data as resulting from a generative model which incorporates both within individual and between-individual variation. In recognition we calculate the likelihood that the differences between face images are entirely due to within-individual variability. We extend this to the non-linear case where an arbitrary face manifold can be described and noise is position-dependent. We also develop a "tied" version of the algorithm that allows explicit comparison across quite different viewing conditions. We demonstrate that our model produces state of the art results for (i) frontal face recognition (ii) face recognition under varying pose.
|Title:||Probabilistic linear discriminant analysis for inferences about identity|
|Event:||11th IEEE International Conference on Computer Vision|
|Location:||Rio de Janeiro, BRAZIL|
|Dates:||2007-10-14 - 2007-10-21|
|Keywords:||SUBSPACE FACE RECOGNITION|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science > Computer Science|
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