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An efficient deep learning model for classification of thermal face images

Basma Abd El-Rahiem (Department of Mathematics and Computer Science, Faculty of Science , Menoufia University, Shebin El-Koom, Egypt)
Ahmed Sedik (Department of the Robotics and Intelligent Machines, Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh, Egypt) (Department of Electrical Engineering, Faculty of Engineering, Minia University, Minia, Egypt)
Ghada M. El Banby (Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufa University, Menouf, Egypt)
Hani M. Ibrahem (Department of Mathematics and Computer Science, Faculty of Science , Menoufia University, Shebin El-Koom, Egypt)
Mohamed Amin (Department of Mathematics and Computer Science, Faculty of Science , Menoufia University, Shebin El-Koom, Egypt)
Oh-Young Song (Sejong University, Seoul, Republic of Korea)
Ashraf A. M. Khalaf (Department of Electrical Engineering, Faculty of Engineering, Minia University, Minia, Egypt)
Fathi E. Abd El-Samie (Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufa University, Menouf, Egypt) (Department of Information Technology, College of Computer and Information sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia)

Journal of Enterprise Information Management

ISSN: 1741-0398

Article publication date: 17 July 2020

Issue publication date: 24 April 2023

863

Abstract

Purpose

The objective of this paper is to perform infrared (IR) face recognition efficiently with convolutional neural networks (CNNs). The proposed model in this paper has several advantages such as the automatic feature extraction using convolutional and pooling layers and the ability to distinguish between faces without visual details.

Design/methodology/approach

A model which comprises five convolutional layers in addition to five max-pooling layers is introduced for the recognition of IR faces.

Findings

The experimental results and analysis reveal high recognition rates of IR faces with the proposed model.

Originality/value

A designed CNN model is presented for IR face recognition. Both the feature extraction and classification tasks are incorporated into this model. The problems of low contrast and absence of details in IR images are overcome with the proposed model. The recognition accuracy reaches 100% in experiments on the Terravic Facial IR Database (TFIRDB).

Keywords

Acknowledgements

This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2019-2016-0-00312) supervised by the IITP (Institute for Information and communications Technology Planning and Evaluation) and by the Faculty Research Fund of Sejong University in 2019.Conflict of interest: The authors declare that there is no conflict of interest for this paper.

Citation

Abd El-Rahiem, B., Sedik, A., El Banby, G.M., Ibrahem, H.M., Amin, M., Song, O.-Y., Khalaf, A.A.M. and Abd El-Samie, F.E. (2023), "An efficient deep learning model for classification of thermal face images", Journal of Enterprise Information Management, Vol. 36 No. 3, pp. 706-717. https://doi.org/10.1108/JEIM-07-2019-0201

Publisher

:

Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited

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