Human face profile recognition by computer
Pattern Recognition
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: The Problem of Compensating for Changes in Illumination Direction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrating Faces and Fingerprints for Personal Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Locating human faces in a cluttered scene
Graphical Models and Image Processing
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Probabilistic Combination of Face and Gait Cues for Identification
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Journal of Cognitive Neuroscience
Fusion of face and speech data for person identity verification
IEEE Transactions on Neural Networks
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
A novel multi-stage classifier for face recognition
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Face image retrieval system using TFV and combination of subimages
VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
Pixel selection based on discriminant features with application to face recognition
Pattern Recognition Letters
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We propose a face recognition method that fuses information acquired from global and local features of the face for improving performance. Principle components analysis followed by Fisher analysis is used for dimensionality reduction and construction of individual feature spaces. Recognition is done by probabilistically fusing the confidence weights derived from each feature space. The performance of the method is validated on FERET and AR databases.