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
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Analytic-to-Holistic Approach for Face Recognition Based on a Single Frontal View
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
The Quotient Image: Class-Based Re-Rendering and Recognition with Varying Illuminations
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Person Identification Using Multiple Cues
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
Face authentication with Gabor information on deformable graphs
IEEE Transactions on Image Processing
Frontal face authentication using morphological elastic graph matching
IEEE Transactions on Image Processing
A shape- and texture-based enhanced Fisher classifier for face recognition
IEEE Transactions on Image Processing
Fusion of face and speech data for person identity verification
IEEE Transactions on Neural Networks
Multiple feature fusion by subspace learning
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
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We propose a novel probabilistic framework that combines information acquired from different facial features for robust face recognition. The features used are the entire face, the edginess image of the face, and the eyes. In the training stage, individual feature spaces are constructed using principal component analysis (PCA) and Fisher's linear discriminant (FLD). By using the distance-in-feature-space (DIFS) values of the training images, the distributions of the DIFS values in each feature space are computed. For a given image, the distributions of the DIFS values yield confidence weights for the three facial features extracted from the image. The final score is computed using a probabilistic fusion criterion and the match with the highest score is used to establish the identity of a person. A new preprocessing scheme for illumination compensation is also advocated. The proposed fusion approach is more reliable than a recognition system which uses only one feature, trained individually. The method is validated on different face datasets, including the FERET database.