1. An efficient deep learning model for classification of thermal face images
- Author
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Basma Abd El-Rahiem, Oh-Young Song, Mohamed Amin, Ashraf A. M. Khalaf, Ahmed Sedik, Ghada M. El Banby, Fathi E. Abd El-Samie, and Hani M. Ibrahem
- Subjects
business.industry ,Computer science ,Deep learning ,General Decision Sciences ,020207 software engineering ,02 engineering and technology ,Facial recognition system ,Management of Technology and Innovation ,Face (geometry) ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Computer vision ,Artificial intelligence ,business ,Information Systems - Abstract
PurposeThe 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/approachA model which comprises five convolutional layers in addition to five max-pooling layers is introduced for the recognition of IR faces.FindingsThe experimental results and analysis reveal high recognition rates of IR faces with the proposed model.Originality/valueA 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).
- Published
- 2020
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