Abstract
In last years, the facial biometry is coming back into education field; it is proposed to control student activities. In this paper, we propose a new face recognition system based on our Local Gradient Probabilistic Pattern (LGPP) and 2D-DWT. Firstly, the almost homogeneous and the picks areas are separate according to LGPP confidence interval. Secondly, the obtained images are decomposed using 2D-DWT in “LL, LH, HL and HH” sub-bands. Thereafter, we extract the features vector using 2D-PCA method applied on the approximation (LL-band). In classification phase, we compare between MLP, Bayesian Networks, SVM and KNN classifiers at features vector. The experimental results show that proposed system improves the recognition rate. Indeed, we reach a rate of 97 % for ORL and 98.8 % for Yale.
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Dahmouni, A., Aharrane, N., Moutaouakil, K.E., Satori, K. (2017). A New Hybrid Face Recognition System via Local Gradient Probabilistic Pattern (LGPP) and 2D-DWT. In: Rocha, Á., Serrhini, M., Felgueiras, C. (eds) Europe and MENA Cooperation Advances in Information and Communication Technologies. Advances in Intelligent Systems and Computing, vol 520. Springer, Cham. https://doi.org/10.1007/978-3-319-46568-5_28
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DOI: https://doi.org/10.1007/978-3-319-46568-5_28
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