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Probabilistic recognition of human faces from video
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Adaptive particle swarm optimization: detection and response to dynamic systems
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Supervised Incremental Learning with the Fuzzy ARTMAP Neural Network
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
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ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
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IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Incremental Boolean combination of classifiers
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Expert Systems with Applications: An International Journal
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Information Sciences: an International Journal
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Information Sciences: an International Journal
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Information Sciences: an International Journal
Gender classification from unaligned facial images using support subspaces
Information Sciences: an International Journal
Black hole: A new heuristic optimization approach for data clustering
Information Sciences: an International Journal
Dynamic multi-objective evolution of classifier ensembles for video face recognition
Applied Soft Computing
Using the idea of the sparse representation to perform coarse-to-fine face recognition
Information Sciences: an International Journal
CAPSO: Centripetal accelerated particle swarm optimization
Information Sciences: an International Journal
Real-time background modeling based on a multi-level texture description
Information Sciences: an International Journal
Fingerprint orientation field reconstruction by weighted discrete cosine transform
Information Sciences: an International Journal
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In many practical applications, new information may emerge from the environment at different points in time after a classification system has originally been deployed. For instance, in biometric systems, new data may be acquired and used to enroll or to update knowledge of an individual. In this paper, an adaptive classification system (ACS) is proposed for video-based face recognition. It combines a fuzzy ARTMAP neural network classifier, dynamic particle swarm optimization (DPSO) algorithm, and a long term memory (LTM). A novel DPSO-based learning strategy is also presented for incremental learning of new data with this ACS. This strategy allows to cojointly optimize the classifier weights, architecture, and user-defined hyperparameters such as classification rate is maximized. Performance of this system is assessed in terms of classification rate and resource requirements for incremental learning of data blocks coming from real-world video data bases. The necessity of a LTM to store validation data is shown empirically for different enrollment and update scenarios. In addition, incremental learning is shown to constitute a dynamic optimization problem where the optimal hyperparameter values change in time. Simulation results indicate that the proposed system can provide a significant higher classification rate than that of fuzzy ARTMAP alone during incremental learning. However, optimization of ACS parameters requires more resources. The ACS needs several training sequences to produce the optimal solution, and adapting fuzzy ARTMAP parameters according to classification rate tends to require more category neurons and training epochs.