Evolutionary Pursuit and Its Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reconfigurable Context-Sensitive Middleware for Pervasive Computing
IEEE Pervasive Computing
A Unified Framework for Subspace Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Random sampling LDA for face recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Designing classifier fusion systems by genetic algorithms
IEEE Transactions on Evolutionary Computation
Switching between selection and fusion in combining classifiers: anexperiment
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper proposes a novel method of classifier selection for efficient object recognition based on evolutionary computation and data context knowledge called Evolvable Classifier Selection. The proposed method tries to distinguish the data characteristics of input image (data contexts) and selects a classifier system accordingly using the genetic algorithm. It stores its experiences in terms of the data context category and the artificial chromosome so that the context knowledge can be accumulated and used later. The proposed method operates in two modes: the evolution mode and the action mode. It can improve its performance incrementally using GA in the evolution mode. Once sufficient context knowledge is accumulated, the method can operate in real-time. The proposed method has been evaluated in the area of face recognition. Data context-awareness, modeling and identification of input data as data context categories, is carried out using SOM.