Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Probabilistic Visual Learning for Object Representation
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
How Should We RepresentFaces for Automatic Recognition?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machines applied to face recognition
Proceedings of the 1998 conference on Advances in neural information processing systems II
Evolutionary Pursuit and Its Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminant Waveletfaces and Nearest Feature Classifiers for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition with one training image per person
Pattern Recognition Letters
Face recognition using the mixture-of-eigenfaces method
Pattern Recognition Letters
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
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Enhanced (PC)2 A for face recognition with one training image per person
Pattern Recognition Letters
A Unified Framework for Subspace Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Face Recognition Based on Gaussian Mixture Models
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Neural Networks - 2005 Special issue: IJCNN 2005
Pattern Recognition Letters
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
(2D)2LDA: An efficient approach for face recognition
Pattern Recognition
Face recognition from a single image per person: A survey
Pattern Recognition
2D and 3D face recognition: A survey
Pattern Recognition Letters
Knowledge and Information Systems
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Subjective Performance of Texture Based Algorithm for Face Verification: The Role of Databases
ICVGIP '08 Proceedings of the 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing
Rapid and brief communication: Two-dimensional FLD for face recognition
Pattern Recognition
2D-LDA: A statistical linear discriminant analysis for image matrix
Pattern Recognition Letters
Mixture-of-Laplacian faces and its application to face recognition
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Handbook of Face Recognition
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Studying the inherently high-dimensional nature of the data in a lower dimensional manifold has become common in recent years. This is generally known as dimensionality reduction. A very interesting strategy for dimensionality reduction is what is known as subspace analysis. Beginning with the Eigenface method, face recognition and in general computer vision has witnessed a growing interest in algorithms that capitalize on this idea and an ample number of such efficient algorithms have been proposed. These algorithms mainly differ in the kind of projection method used (linear or non-linear) or in the criterion employed for classification. The objective of this paper is to provide a comprehensive performance evaluation of about twenty five different subspace algorithms under several important real time test conditions. For this purpose, we have considered the performance of these algorithms on data taken from four standard face and object databases namely ORL, Yale, FERET and the COIL-20 object database. This paper also presents some theoretical aspects of the algorithm and the analysis of the simulations carried out.