Optimal combinations of pattern classifiers
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Approximate nearest neighbor algorithms for Frechet distance via product metrics
Proceedings of the eighteenth annual symposium on Computational geometry
Fuzzy ARTMAP network with evolutionary learning
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 06
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 investigate how to preprocess method from input images for robust face recognition varying illumination environments. By training the different classifiers with different clusters of training data and adopting fusion method considering fitness correlation between clusters we found out better recognition performance than combining classifiers fed with same data. The proposed method tries to provide adaptive preprocessing as well as by exploring the filter selection and fusion based on illumination cluster. Illuminant based clustering is enhanced face recognition ratio. Face image is clustered several cluster unsupervised or statistical method and we adopt adaptive filter each cluster. In this paper, some cluster is preprocessed by single filter others and some cluster adopted preprocessing by filter fusion. We found that the performance of individual filtering methods for image enhancement is highly depending upon face image cluster. Also, in this paper we present the recognition system using the table of fitness correlations between clusters for combining the results from the individual clusters. We present examples from real applications for bad illuminant face images.