Feature Selection: Evaluation, Application, and Small Sample Performance
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
Face Recognition: The Problem of Compensating for Changes in Illumination Direction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition with one training image per person
Pattern Recognition Letters
Modular learning through output space decomposition
Modular learning through output space decomposition
Face recognition using LDA mixture model
Pattern Recognition Letters
Component-based LDA Method for Face Recognition with One Training Sample
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Unified Subspace Analysis for Face Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Distance measures for PCA-based face recognition
Pattern Recognition Letters
Journal of Cognitive Neuroscience
Face recognition with radial basis function (RBF) neural networks
IEEE Transactions on Neural Networks
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
Face recognition from a single image per person: A survey
Pattern Recognition
On solving the face recognition problem with one training sample per subject
Pattern Recognition
Gaussian kernel optimization for pattern classification
Pattern Recognition
Color face recognition for degraded face images
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Uncorrelated multilinear principal component analysis for unsupervised multilinear subspace learning
IEEE Transactions on Neural Networks
Interesting faces: A graph-based approach for finding people in news
Pattern Recognition
Sparse representation-based face recognition for one training image per person
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
A novel feature vectors construction approach for face recognition
Transactions on computational science XI
Adaptive discriminant learning for face recognition
Pattern Recognition
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In realistic face recognition applications, such as surveillance photo identification, supervised learning algorithms usually fail when only one training sample per subject is available. The lack of training samples and the considerable image variations due to aging, illumination and pose variations, make recognition a challenging task. This letter proposes a development of the traditional eigenface solution by applying a feature selection process on the extracted eigenfaces. The proposal calls for the establishment of a feature subspace in which the intrasubject variation is minimized while the intersubject variation is maximized. Extensive experimentation following the FERET evaluation protocol suggests that in the scenario considered here, the proposed scheme improves significantly the recognition performance of the eigenface solution and outperforms other state-of-the-art methods.