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
A Unified Framework for Subspace Face Recognition
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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In this paper, we proposed evolutionary filter and classifier ensemble. We designed the face recognition system that is consists of training module and testing module. The training module is evolution step, that process make filter and classifier combination. In testing step, we identified face recognition using knowledge from made training step. The filters are applied preprocessing step. Captured images are varying illuminant images so we proposed evolutionary preprocessing filter combinations and classifier ensemble for data context. The Proposed classifier selection for efficient object recognition based on evolutionary computation and data context knowledge called context based evolutionary system. In proposed method, we distinguish the data characteristics of input image and filter selects a classifier system accordingly using evolutionary algorithm. The proposed method is high more than single method.