This paper is devoted to the study of supervised learning methods as part of pattern recognition and especially the Amazigh Characters Recognition. The goal is to compare a partial list of the popular automatic classification methods, and test the performance of the proposed features set extracted from isolated characters using statistical methods with these different classifiers. In Experimental evaluation, several runs have been conducted for the different algorithms and the best accuracy observed is for the multilayer perceptron with a recognition rate about 96,47% which is very satisfactory.
Facial biometrics is an active modality that uses the face characteristics as argument of person identification. In this paper, we propose a new face recognition system basing on the Local Binary Probabilistic Pattern (LBPP) face representation and the global 2D-DCT frequency methods. The Local Binary Probabilistic Pattern is an alternative of the famous LBP descriptor which uses the confidence interval concept to evaluate the current pixel. Then the LBPP transformed images are decomposed in the frequency domain at 2D-DCT method to build a reduce features vector. The suggested approach is tested on ORL and Yale databases. The obtained results are very encouraging: 95.5% for ORL and 100% for Yale databases recognition rate.
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