Optimized superpixel and AdaBoost classifier for human thermal face recognition

Ibrahim, A, Tharwat, A and Gaber, T ORCID: https://orcid.org/0000-0003-4065-4191 2018, 'Optimized superpixel and AdaBoost classifier for human thermal face recognition' , Signal, Image and Video Processing, 12 (4) , pp. 711-719.

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Infrared spectrum-based human recognition systems offer straightforward and robust solutions for achieving an excellent performance in uncontrolled illumination. In this paper, a human thermal face recognition model is proposed. The model consists of four main steps. Firstly, the grey wolf optimization algorithm is used to find optimal superpixel parameters of the quick-shift segmentation method. Then, segmentation-based fractal texture analysis algorithm is used for extracting features and the rough set-based methods are used to select the most discriminative features. Finally, the AdaBoost classifier is employed for the classification process. For evaluating our proposed approach, thermal images from the Terravic Facial infrared dataset were used. The experimental results showed that the proposed approach achieved (1) reasonable segmentation results for the indoor and outdoor thermal images, (2) accuracy of the segmented images better than the non-segmented ones, and (3) the entropy-based feature selection method obtained the best classification accuracy. Generally, the classification accuracy of the proposed model reached to 99% which is better than some of the related work with around 5%.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title: Signal, Image and Video Processing
Publisher: Taylor & Francis
ISSN: 1863-1703
Related URLs:
Depositing User: Dr. Tarek Gaber
Date Deposited: 19 Aug 2019 13:47
Last Modified: 16 Feb 2022 02:31
URI: http://usir.salford.ac.uk/id/eprint/52076

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