Discrete-time signal processing
Discrete-time signal processing
Inference in model-based cluster analysis
Statistics and Computing
Effective Implementation of Linear Discriminant Analysis for Face Recognition and Verification
CAIP '99 Proceedings of the 8th International Conference on Computer Analysis of Images and Patterns
The CMU Pose, Illumination, and Expression (PIE) Database
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Real time face authentication system using autoassociative neural network models
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Minimax Entropy Principle and Its Application to Texture Modeling
Neural Computation
Deformable templates for face recognition
Journal of Cognitive Neuroscience
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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It has been established that information distinguishing one human face from another is contained to a large extent in the Fourier domain phase component of the facial image. However, to date, formal statistical models for this component have not been deployed in face recognition tasks. In this paper we introduce a model-based approach using Gaussian mixture models (GMM) for the phase component for performing human identification. Classification and verification are performed using a MAP estimate and we show that we are able to achieve identification error rates as low as 2% and verification error rates as low as 0.3% on a database with 65 individuals with extreme illumination variations. The proposed method is easily able to deal with other distortions such as expressions and poses, and hence this establishes its robustness to intra-personal variations. A potential use of the method in illumination normalization is also discussed.