Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
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 Using Line Edge Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Lambertian Reflectance and Linear Subspaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Eigenfeature Regularization and Extraction in Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
SVM-based feature extraction for face recognition
Pattern Recognition
Dual-space linear discriminant analysis for face recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Performance evaluation of subspace methods to tackle small sample size problem in face recognition
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Biological brain and binary code: quality of coding for face recognition
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
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Due to high dimensionality of face images and finite number of training samples, the linear subspace technique for face recognition pose challenges for better performance. In this paper, we compare the performance of linear subspace methods which involve the computation of scatter matrices for face recognition under illumination variation. The performance of these methods is evaluated in terms of classification accuracy, computational training and testing time. Extensive empirical experiments are performed to compare the performance using AR, Pie, Yale and YaleB face databases. In absence of sufficient number of training samples, classification accuracy of linear subspace methods deteriorate. Experimental results show that the performance of Dual LDA is best in terms of average classification accuracy. It is also observed that Fisherface takes minimum training time and both ERE and SVM takes minimum testing time. No linear subspace method outperforms others in terms of all performance measures.