Dynamic training using multistage clustering for face recognition
Pattern Recognition
Generalized Fourier Descriptors with Applications to Objects Recognition in SVM Context
Journal of Mathematical Imaging and Vision
Deep learning from temporal coherence in video
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Bayesian Face Recognition Based on Markov Random Field Modeling
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
ICA Based on KPCA and Hierarchical RBF Network for Face Recognition
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Contextual constraints based linear discriminant analysis
Pattern Recognition Letters
A novel training weighted ensemble (TWE) with application to face recognition
Applied Soft Computing
Computer Vision and Image Understanding
Face recognition using DCT and hierarchical RBF model
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Performance evaluation of face recognition based on PCA, LDA, ICA and hidden markov model
ICDEM'10 Proceedings of the Second international conference on Data Engineering and Management
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We propose a hybrid face recognition method that combines holistic and feature analysis-based approaches using a Markov random field (MRF) model. The face images are divided into small patches, and the MRF model is used to represent the relationship between the image patches and the patch ID's. The MRF model is first learned from the training image patches, given a test image. The most probable patch ID's are then inferred using the belief propagation (BP) algorithm. Finally, the ID of the test image is determined by a voting scheme from the estimated patch ID's. Experimental results on several face datasets indicate the significant potential of our method.