A multiexpert collaborative biometric system for people identification
Journal of Visual Languages and Computing
Normal maps vs. visible images: Comparing classifiers and combining modalities
Journal of Visual Languages and Computing
Fast algorithm for updating the discriminant vectors of dual-space LDA
IEEE Transactions on Information Forensics and Security
An incremental learning algorithm of recursive Fisher linear discriminant
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Incremental two-dimensional linear discriminant analysis with applications to face recognition
Journal of Network and Computer Applications
The face recognition algorithm based on offset difference of double subspace
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Image ratio features for facial expression recognition application
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Robust classifiers for data reduced via random projections
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Convergence proof of matrix dynamics for online linear discriminant analysis
Journal of Multivariate Analysis
Kernel discriminant transformation for image set-based face recognition
Pattern Recognition
Effective monitoring by efficient fingerprint matching using a forest of NAQ-trees
Journal of Intelligent Information Systems
Least squares online linear discriminant analysis
Expert Systems with Applications: An International Journal
Fast and robust face recognition for incremental data
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
Incremental complete LDA for face recognition
Pattern Recognition
Facial image analysis using subspace segregation based on class information
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Incremental face recognition for large-scale social network services
Pattern Recognition
Incremental learning of complete linear discriminant analysis for face recognition
Knowledge-Based Systems
Incremental threshold learning for classifier selection
Neurocomputing
Concurrency and Computation: Practice & Experience
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Dimensionality reduction methods have been successfully employed for face recognition. Among the various dimensionality reduction algorithms, linear (Fisher) discriminant analysis (LDA) is one of the popular supervised dimensionality reduction methods, and many LDA-based face recognition algorithms/systems have been reported in the last decade. However, the LDA-based face recognition systems suffer from the scalability problem. To overcome this limitation, an incremental approach is a natural solution. The main difficulty in developing the incremental LDA (ILDA) is to handle the inverse of the within-class scatter matrix. In this paper, based on the generalized singular value decomposition LDA (LDA/GSVD), we develop a new ILDA algorithm called GSVD-ILDA. Different from the existing techniques in which the new projection matrix is found in a restricted subspace, the proposed GSVD-ILDA determines the projection matrix in full space. Extensive experiments are performed to compare the proposed GSVD-ILDA with the LDA/GSVD as well as the existing ILDA methods using the face recognition technology face database and the Carneggie Mellon University Pose, Illumination, and Expression face database. Experimental results show that the proposed GSVD-ILDA algorithm gives the same performance as the LDA/GSVD with much smaller computational complexity. The experimental results also show that the proposed GSVD-ILDA gives better classification performance than the other recently proposed ILDA algorithms.