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Face recognition using multi-class Logical Analysis of Data

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Abstract

This paper addresses the applicability of multi-class Logical Analysis of Data (LAD) as a face recognition technique (FRT). This new classification technique has already been applied in the field of biomedical and mechanical engineering as a diagnostic technique; however, it has never been used in the face recognition literature. We explore how Eigenfaces and Fisherfaces merged to multi-class LAD can be leveraged as a proposed FRT, and how it might be useful compared to other approaches. The aim is to build a single multi-class LAD decision model that recognizes images of the face of different persons. We show that our proposed FRT can effectively deal with multiple changes in the pose and facial expression, which constitute critical challenges in the literature. Comparisons are made both from analytical and practical point of views. The proposed model improves the classification of Eigenfaces and Fisherfaces with minimum error rate.

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Authors and Affiliations

  1. Industrial Engineering and Applied Mathematics Department, École Polytechnique de Montréal C.P. 6079, Succ. Centre-Ville, H3C 3A7, Montréal, Québec, Canada

    A. Ragab, Soumaya Yacout & Mohamed-Salah Ouali

  2. Concordia Institute for Information Systems Engineering, Concordia University, 1455 De Maisonneuve Blvd. Ouest, H3G 1M8, Montréal, Québec, Canada

    Xavier de Carné de Carnavalet

Authors
  1. A. Ragab
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  2. Xavier de Carné de Carnavalet
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  3. Soumaya Yacout
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  4. Mohamed-Salah Ouali
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Correspondence to A. Ragab.

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Ahmed Ragab received a B.Sc. in Electronic Engineering and the M.Sc. in Control Engineering from Faculty of Electronic Engineering, Minuf, Egypt in 2003 and 2007, respectively. He received the PhD. degree in Industrial Engineering from École Polytechnique de Montreal, Canada in 2014. His research interests are: Machine Learning and Pattern Recognition, Condition-Based Maintenance, Fault Diagnosis and Prognosis, Discrete Event Systems, Control Systems, Petri Nets, Timed Automata, and Operations Research

Xavier de Carné de Carnavalet received in 2014 a Diplome d’Ingénieur (M.Sc.) from École Supérieure d’Informatique, Électronique et Automatique, Paris, France, and anM.A.Sc. in Information Systems Security from Concordia University, Montreal, QC, Canada. His research interests are: privacy, passwords and authentication, TLS, trusted computing, reverse-engineering, and machine learning applications to information systems security.

Soumaya Yacout is Professor of Industrial Engineering and Operations Research at École Polytechnique de Montréal in Canada since 1999. She received a D.Sc. in Operations Research in 1985, and a M.Sc. in Industrial Engineering in 1979. Her research interests include Condition- Based Maintenance and optimization of decision making for product quality. She is a senior member of the American Society for Quality (ASQ) and the Canadian Operations Research Society (CORS).

Mohamed-Salah Ouali is a Professor of Industrial Engineering at the École Polytechnique de Montréal. His research interests focus on reliability modeling, multiple failure modes diagnosis, and long-term fleet maintenance. He obtained his Doctorate degree from the Institut National Polytechnique de Grenoble, France, in 1996, and worked as assistant professor at Moncton University, New-Brunswich, from 1998 to 2000. He is a member of the Interuniversity Research Centre on Enterprise networks, Logistics and Transport (CIRRELT) and the Ordre des Ingenieurs du Québec, Canada.

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Ragab, A., de Carné de Carnavalet, X., Yacout, S. et al. Face recognition using multi-class Logical Analysis of Data. Pattern Recognit. Image Anal. 27, 276–288 (2017). https://doi.org/10.1134/S1054661817020092

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