Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolutionary Pursuit and Its Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Digital Image Processing
A Bayesian Similarity Measure for Direct Image Matching
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Fuzzy ARTMAP network with evolutionary learning
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 06
Journal of Cognitive Neuroscience
Switching between selection and fusion in combining classifiers: anexperiment
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
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Adaptation to dynamically changing environment is very important since advanced applications become pervasive and ubiquitous. This paper addresses a novel method of adaptive object recognition using environmental context-awareness and genetic algorithm and t-test. The proposed method tries to distinguish the category of input environment and decides an optimal classifier combination structure accordingly by GA and t-test. It stores its experiences in terms of the data context categories and the evolved artificial chromosomes so that the evolutionary knowledge can be used later. The proposed method has been evaluated in the area of face recognition. Most previous face recognition schemes define their system structures at the design phases, and the structures are not adaptive during operation. Such approaches usually show vulnerability under varying illumination environment. The context-awareness, modeling and identification of input data as context categories, is carried out by Fuzzy ART. The face data context is described based on the image attributes of light direction and brightness. The superiority of the proposed system is shown using four data sets: Inha, FERET and Yale database.