Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Subspace Classification for Face Recognition
ECCV '02 Proceedings of the International ECCV 2002 Workshop Copenhagen on Biometric Authentication
Discriminant Analysis of Principal Components for Face Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
View-Subspace Analysis of Multi-View Face Patterns
RATFG-RTS '01 Proceedings of the IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems (RATFG-RTS'01)
Fast features for face authentication under illumination direction changes
Pattern Recognition Letters
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
A Bayesian Similarity Measure for Direct Image Matching
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Journal of Cognitive Neuroscience
Random subspace for an improved BioHashing for face authentication
Pattern Recognition Letters
Generalized Needleman-Wunsch algorithm for the recognition of T-cell epitopes
Expert Systems with Applications: An International Journal
Ensemble of multiple Palmprint representation
Expert Systems with Applications: An International Journal
Classification of mycobacterium tuberculosis in images of ZN-stained sputum smears
IEEE Transactions on Information Technology in Biomedicine
A new encoding technique for peptide classification
Expert Systems with Applications: An International Journal
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In this paper we introduce a new face recognition approach based on the representation of each individual by several lower dimensional subspaces obtained by an unsupervised clustering of different poses: this provides a higher robustness to face variations than traditional subspace approaches. A set of subspaces is created for each individual, starting from a feature vector extracted through a bank of Gabor filters and non-linear Fisher transform. Extensive experiments carried out on the FERET database of faces, which is the most common benchmark in this area, prove the advantages of the proposed approach when compared with other well-known techniques. These results confirm the robustness of our approach against appearance variations due to expression, illumination and pose changes or to aging effects.