Ensembling neural networks: many could be better than all
Artificial Intelligence
Online Fingerprint Template Improvement
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering ensembles of neural network models
Neural Networks
Probabilistic recognition of human faces from video
Computer Vision and Image Understanding - Special issue on Face recognition
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Selecting Models from Videos for Appearance-Based Face Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Video-Based Framework for Face Recognition in Video
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Particle swarm with speciation and adaptation in a dynamic environment
Proceedings of the 8th annual conference on Genetic and evolutionary computation
An analysis of diversity measures
Machine Learning
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
Moderate diversity for better cluster ensembles
Information Fusion
Adaptive particle swarm optimization: detection and response to dynamic systems
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Dynamic integration of classifiers for handling concept drift
Information Fusion
Multi-strategy ensemble particle swarm optimization for dynamic optimization
Information Sciences: an International Journal
Particle Swarms for Multimodal Optimization
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
A methodology for rapid illumination-invariant face recognition using image processing filters
Computer Vision and Image Understanding
Incremental construction of classifier and discriminant ensembles
Information Sciences: an International Journal
Person recognition using facial video information: A state of the art
Journal of Visual Languages and Computing
Challenges and Research Directions for Adaptive Biometric Recognition Systems
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Triggered Memory-Based Swarm Optimization in Dynamic Environments
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Incremental adaptation of fuzzy ARTMAP neural networks for video-based face classification
CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
Video-based face recognition using adaptive hidden markov models
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
An adaptive classification system for video-based face recognition
Information Sciences: an International Journal
Cooperative coevolution of artificial neural network ensembles for pattern classification
IEEE Transactions on Evolutionary Computation
Face recognition with radial basis function (RBF) neural networks
IEEE Transactions on Neural Networks
Ensemble-based discriminant learning with boosting for face recognition
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Particle Swarm Optimization to Design Ideotypes for Sustainable Fruit Production Systems
International Journal of Swarm Intelligence Research
Dynamic multi-objective evolution of classifier ensembles for video face recognition
Applied Soft Computing
Hierarchical Particle Swarm Optimization with Ortho-Cyclic Circles
Expert Systems with Applications: An International Journal
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In many real-world applications, pattern recognition systems are designed a priori using limited and imbalanced data acquired from complex changing environments. Since new reference data often becomes available during operations, performance could be maintained or improved by adapting these systems through supervised incremental learning. To avoid knowledge corruption and sustain a high level of accuracy over time, an adaptive multiclassifier system (AMCS) may integrate information from diverse classifiers that are guided by a population-based evolutionary optimization algorithm. In this paper, an incremental learning strategy based on dynamic particle swarm optimization (DPSO) is proposed to evolve heterogeneous ensembles of classifiers (where each classifier corresponds to a particle) in response to new reference samples. This new strategy is applied to video-based face recognition, using an AMCS that consists of a pool of fuzzy ARTMAP (FAM) neural networks for classification of facial regions, and a niching version of DPSO that optimizes all FAM parameters such that the classification rate is maximized. Given that diversity within a dynamic particle swarm is correlated with diversity within a corresponding pool of base classifiers, DPSO properties are exploited to generate and evolve diversified pools of FAM classifiers, and to efficiently select ensembles among the pools based on accuracy and particle swarm diversity. Performance of the proposed strategy is assessed in terms of classification rate and resource requirements under different incremental learning scenarios, where new reference data is extracted from real-world video streams. Simulation results indicate the DPSO strategy provides an efficient way to evolve ensembles of FAM networks in an AMCS. Maintaining particle diversity in the optimization space yields a level of accuracy that is comparable to AMCS using reference ensemble-based and batch learning techniques, but requires significantly lower computational complexity than assessing diversity among classifiers in the feature or decision spaces.