Threshold-optimized decision-level fusion and its application to biometrics
Pattern Recognition
Group-specific face verification using soft biometrics
Journal of Visual Languages and Computing
Face recognition using immune network based on principal component analysis
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Multimodal biometric system using rank-level fusion approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Fusion of covariance matrices of PCA and FLD
Pattern Recognition Letters
PCA based immune networks for human face recognition
Applied Soft Computing
Improvement on null space LDA for face recognition: a symmetry consideration
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
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Although many algorithms have been proposed, face recognition and verification systems can guarantee a good level of performances only for controlled environments. In order to improve the performance and robustness of face recognition and verification systems, multi-modal and mono-modal systems based on the fusion of multiple recognisers using different or similar biometrics have been proposed, especially for verification purposes. In this paper, a recognition and verification system based on the combination of two well-known appearance-based representations of the face, namely, principal component analysis (PCA) and linear discriminant analysis (LDA), is proposed. Both PCA and LDA are used as feature extractors from frontal view images. The benefits of such a fusion are shown for different environmental conditions, namely, ideal conditions, characterised by a very limited variability of environmental parameters, and real conditions with a large variability of lighting, scale and facial expression.