Skin identification using deep convolutional neural network

Maktab Dar Oghaz, Mahdi, Argyriou, Vasileios, Monekosso, Dorothy and Remagnino, Paolo (2019) Skin identification using deep convolutional neural network. In: 14th International Symposium on Visual Computing, ISVC 2019; 07 - 09 Oct 2019, Lake Tahoe, NV, U.S..

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Skin identification can be used in several security applications such as border’s security checkpoints and facial recognition in bio-metric systems. Traditional skin identification techniques were unable to deal with the high complexity and uncertainty of human skin in uncontrolled environments. To address this gap, this research proposes a new skin identification technique using deep convolutional neural network. The proposed sequential deep model consists of three blocks of convolutional layers, followed by a series of fully connected layers, optimized to maximize skin texture classification accuracy. The proposed model performance has been compared with some of the well-known texture-based skin identification techniques and delivered superior results in terms of overall accuracy. The experiments were carried out over two datasets including FSD Benchmark dataset as well as an in-house skin texture patch dataset. Results show that the proposed deep skin identification model with highest reported accuracy of 0.932 and minimum loss of 0.224 delivers reliable and robust skin identification.

Item Type: Conference or Workshop Item (Paper)
Event Title: 14th International Symposium on Visual Computing, ISVC 2019
Organising Body: International Symposium on Visual Computing (ISVC)
Additional Information: Published in: Bebis, George, Boyle, Richard, Parvin, Bahram, Koracin, Darko, Ushizima, Daniela, Chai, Sek, Sueda, Shinjiro, Lin, Xin, Lu, Aidong, Thalmann, Daniel, Wang, Chaoli, Xu, Panpan (eds.) (2019) 14th International Symposium on Visual Computing, ISVC 2019, Lake Tahoe, NV, USA, October 7–9, 2019, Proceedings, Part I. Cham, Switzerland : Springer International Publishing. pp. 181-193. (Lecture Notes in Computer Science, no. 11844, part of the Image Processing, Computer Vision, Pattern Recognition, and Graphics book sub-series, LNIP, volume 11844) ISSN (print) 0302-9743 ISBN 9783030337193.
Uncontrolled Keywords: Skin texture analysis; Convolutional Neural Networks; Deep learning Segmentation
Research Area: Computer science and informatics
Faculty, School or Research Centre: Faculty of Science, Engineering and Computing
Depositing User: Philip Keates
Date Deposited: 26 Feb 2020 10:52
Last Modified: 26 Feb 2020 10:52

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