An on-line learning method for face association in personal photo collection
Image and Vision Computing
Class dependent factor analysis and its application to face recognition
Pattern Recognition
Robust pose invariant face recognition using coupled latent space discriminant analysis
Computer Vision and Image Understanding
Deep nonlinear metric learning with independent subspace analysis for face verification
Proceedings of the 20th ACM international conference on Multimedia
Bayesian face revisited: a joint formulation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Pairwise support vector machines and their application to large scale problems
The Journal of Machine Learning Research
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Many face recognition algorithms use “distance-based” methods: Feature vectors are extracted from each face and distances in feature space are compared to determine matches. In this paper, we argue for a fundamentally different approach. We consider each image as having been generated from several underlying causes, some of which are due to identity (latent identity variables, or LIVs) and some of which are not. In recognition, we evaluate the probability that two faces have the same underlying identity cause. We make these ideas concrete by developing a series of novel generative models which incorporate both within-individual and between-individual variation. We consider both the linear case, where signal and noise are represented by a subspace, and the nonlinear 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 of faces across quite different viewing conditions. We demonstrate that our model produces results that are comparable to or better than the state of the art for both frontal face recognition and face recognition under varying pose.