Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fractal image compression: theory and application
Fractal image compression: theory and application
Using Discriminant Eigenfeatures for Image Retrieval
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
Information Fusion in Biometrics
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Face recognition by fractal transformations
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
2D and 3D face recognition: A survey
Pattern Recognition Letters
Face, Ear and Fingerprint: Designing Multibiometric Architectures
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
Journal of Cognitive Neuroscience
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving fusion with margin-derived confidence in biometric authentication tasks
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Face recognition using the weighted fractal neighbor distance
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Kernel machine-based one-parameter regularized Fisher discriminant method for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
GA-fisher: a new LDA-based face recognition algorithm with selection of principal components
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A novel incremental principal component analysis and its application for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Incremental Linear Discriminant Analysis for Face Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
FIRE: fractal indexing with robust extensions for image databases
IEEE Transactions on Image Processing
Orthogonal Laplacianfaces for Face Recognition
IEEE Transactions on Image Processing
Face recognition: a convolutional neural-network approach
IEEE Transactions on Neural Networks
Face recognition/detection by probabilistic decision-based neural network
IEEE Transactions on Neural Networks
Face recognition with radial basis function (RBF) neural networks
IEEE Transactions on Neural Networks
Automatic template labeling in extensible multiagent biometric systems
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
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Present identification through single-biometric systems suffer from a number of limitations, due to the fact that no single bodily or behavioral feature is able to satisfy at the same time acceptability, speed and reliability constraints of authentication in real applications. Multibiometric systems can solve a number of problems of single-biometry approaches. A crucial issue to be investigated relates to how results from different systems should be evaluated and fused, in order to obtain an as reliable as possible global response. A further source of flaws for present systems, both single-biometric and multibiometric, can be found in the lack of dynamic update of parameters, which does not allow them to adapt to changes in the working settings. They are generally calibrated once and for all, so that they are tuned and optimized with respect to specific conditions. In this work, we investigate an architecture where single-biometry subsystems work in parallel, yet exchanging information at fixed points, according to the N-Cross Testing Protocol. In particular, the integrated subsystems work on the same biometric feature, the face in this case, yet exploiting different classifiers. Notice that such specific configuration is interesting to underline how the strengths of one classifier can compensate for flaws of other classifiers, so that the final result is more accurate and reliable. Moreover, parameters of each subsystem are also dynamically optimized according to the behavior of all the others. This is achieved by an additional component, the supervisor module, which analyzes the responses from all subsystems and modifies the degree of reliability required from each of them to accept the respective responses. In this way subsystems collaborate at a twofold level, both for returning a common answer and for tuning to changing operating conditions. The paper explores the combination of these two novel approaches, demonstrating that component collaboration increases system accuracy and allows identifying unstable subsystems.