Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to variational methods for graphical models
Learning in graphical models
Face Identification across Different Poses and Illuminations with a 3D Morphable Model
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Guide to Biometrics
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Probabilistic identity characterization for face recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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We present a new approach to biometrics that makes probabilistic inferences about matching without ever estimating an identity "template". The biometric data is considered to have been created by a noisy generative process. This process consists of (i) a deterministic component, which depends entirely on an underlying representation of identity and (ii) a stochastic component which accounts for the fact that two biometric samples from the same person are not identical. In recognition, we make inferences about whether the underlying identity representation is the same without ever estimating it. Instead we treat identity as fundamentally uncertain and consider all possible values in our decision. We demonstrate these ideas with toy examples from face recognition. We compare our approach to the class-conditional viewpoint.