An efficient deep learning model for classification of thermal face images

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).

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