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Robust Face Recognition Using Local Gradient Probabilistic Pattern (LGPP)

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Proceedings of the Mediterranean Conference on Information & Communication Technologies 2015

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 380))

Abstract

In this work, we propose a new local pattern for face recognition, named Local Gradient Probabilistic Pattern (LGPP). It is an extension of Local Gradient Pattern (LGP) that uses a very important result of probability theory; it is the law large numbers. In this direction, the distribution of the gray levels on a face image follows a law of probability, which is the sum of several normal laws. The current pixel will be evaluated by the confidence interval concept. The suggested model is merged with the most known algorithms of data analysis in the face recognition field, in particular the PCA, the LDA, the 2DPCA and the 2DLDA. The tests carried out on the ORL and YALE databases show the effectiveness of LGPP+2DPCA and LGPP+2DLDA systems. The experimental exactitude is of 96 %.

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Authors and Affiliations

  1. LIIAN, Department of Mathematics and Computer Science, Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Atlas-Fez, B.P 1796, Dhar-Mahraz, Morocco

    Abdellatif Dahmouni & Khalid Satori

  2. ENSAH, National School of Applied Sciences Al Hoceima, Ajdir, BP 03, Al-Hoceima, Morocco

    Karim El Moutaouakil

Authors
  1. Abdellatif Dahmouni
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  2. Karim El Moutaouakil
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  3. Khalid Satori
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Corresponding author

Correspondence to Abdellatif Dahmouni .

Editor information

Editors and Affiliations

  1. National School of Applied Sciences of Tangier, Abdelmalek Essaadi University, Tangier, Morocco

    Ahmed El Oualkadi

  2. Technopark el Ghazala, Sup’Com, Ghazala, Tunisia

    Fethi Choubani

  3. National School of Applied Sciences, Oujda, Mohammed Premier University, Oujda, Morocco

    Ali El Moussati

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Dahmouni, A., Moutaouakil, K.E., Satori, K. (2016). Robust Face Recognition Using Local Gradient Probabilistic Pattern (LGPP). In: El Oualkadi, A., Choubani, F., El Moussati, A. (eds) Proceedings of the Mediterranean Conference on Information & Communication Technologies 2015. Lecture Notes in Electrical Engineering, vol 380. Springer, Cham. https://doi.org/10.1007/978-3-319-30301-7_29

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  • DOI: https://doi.org/10.1007/978-3-319-30301-7_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30299-7

  • Online ISBN: 978-3-319-30301-7

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