Skin identification using deep convolutional neural network

Mahdi Maktab Dar Oghaz; Vasileios Argyriou; Dorothy Monekosso; Paolo Remagnino

doi:10.1007/978-3-030-33720-9_14

Skin identification using deep convolutional neural network

Mahdi Maktab Dar Oghaz
, Vasileios Argyriou
, Dorothy Monekosso
, Paolo Remagnino

Leeds Beckett University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	Advances in Visual Computing
Subtitle of host publication	14th International Symposium on Visual Computing, ISVC 2019, Lake Tahoe, NV, USA, October 7–9, 2019, Proceedings, Part I
Editors	George Bebis, Richard Boyle, Bahram Parvin, Darko Koracin, Daniela Ushizima, Sek Chai, Shinjiro Sueda, Xin Lin, Aidong Lu, Daniel Thalmann, Chaoli Wang, Panpan Wu
Place of Publication	Cham
Publisher	Springer Nature
Pages	181–193
Volume	11844
Edition	1
ISBN (Electronic)	978-3-030-33720-9
ISBN (Print)	978-3-030-33719-3
DOIs	https://doi.org/10.1007/978-3-030-33720-9_14
Publication status	Published - 21 Oct 2019
Event	14th International Symposium on Visual Computing, ISVC 2019 - Lake Tahoe, NV, U.S. Duration: 7 Oct 2019 → 9 Oct 2019

Publication series

Name	Lecture Notes in Computer Science
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th International Symposium on Visual Computing, ISVC 2019
Period	7/10/19 → 9/10/19

Bibliographical note

Organising Body: International Symposium on Visual Computing (ISVC)

Keywords

Computer science and informatics
Convolutional Neural Networks
Deep learning Segmentation
Skin texture analysis

Access to Document

10.1007/978-3-030-33720-9_14

Cite this

Maktab Dar Oghaz, M., Argyriou, V., Monekosso, D., & Remagnino, P. (2019). Skin identification using deep convolutional neural network. In G. Bebis, R. Boyle, B. Parvin, D. Koracin, D. Ushizima, S. Chai, S. Sueda, X. Lin, A. Lu, D. Thalmann, C. Wang, & P. Wu (Eds.), Advances in Visual Computing: 14th International Symposium on Visual Computing, ISVC 2019, Lake Tahoe, NV, USA, October 7–9, 2019, Proceedings, Part I (1 ed., Vol. 11844, pp. 181–193). (Lecture Notes in Computer Science). Springer Nature. https://doi.org/10.1007/978-3-030-33720-9_14

Maktab Dar Oghaz, Mahdi ; Argyriou, Vasileios ; Monekosso, Dorothy et al. / Skin identification using deep convolutional neural network. Advances in Visual Computing: 14th International Symposium on Visual Computing, ISVC 2019, Lake Tahoe, NV, USA, October 7–9, 2019, Proceedings, Part I. editor / George Bebis ; Richard Boyle ; Bahram Parvin ; Darko Koracin ; Daniela Ushizima ; Sek Chai ; Shinjiro Sueda ; Xin Lin ; Aidong Lu ; Daniel Thalmann ; Chaoli Wang ; Panpan Wu. Vol. 11844 1. ed. Cham : Springer Nature, 2019. pp. 181–193 (Lecture Notes in Computer Science).

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title = "Skin identification using deep convolutional neural network",

abstract = "Skin identification can be used in several security applications such as border{\textquoteleft}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.",

keywords = "Computer science and informatics, Convolutional Neural Networks, Deep learning Segmentation, Skin texture analysis",

author = "\{Maktab Dar Oghaz\}, Mahdi and Vasileios Argyriou and Dorothy Monekosso and Paolo Remagnino",

note = "Organising Body: International Symposium on Visual Computing (ISVC); 14th International Symposium on Visual Computing, ISVC 2019 ; Conference date: 07-10-2019 Through 09-10-2019",

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editor = "Bebis, \{George \} and Boyle, \{Richard \} and Parvin, \{Bahram \} and Koracin, \{Darko \} and Ushizima, \{Daniela \} and Sek Chai and Shinjiro Sueda and Xin Lin and Aidong Lu and Daniel Thalmann and Chaoli Wang and Wu, \{Panpan \}",

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Maktab Dar Oghaz, M, Argyriou, V, Monekosso, D & Remagnino, P 2019, Skin identification using deep convolutional neural network. in G Bebis, R Boyle, B Parvin, D Koracin, D Ushizima, S Chai, S Sueda, X Lin, A Lu, D Thalmann, C Wang & P Wu (eds), Advances in Visual Computing: 14th International Symposium on Visual Computing, ISVC 2019, Lake Tahoe, NV, USA, October 7–9, 2019, Proceedings, Part I. 1 edn, vol. 11844, Lecture Notes in Computer Science, Springer Nature, Cham, pp. 181–193, 14th International Symposium on Visual Computing, ISVC 2019, 7/10/19. https://doi.org/10.1007/978-3-030-33720-9_14

Skin identification using deep convolutional neural network. / Maktab Dar Oghaz, Mahdi; Argyriou, Vasileios; Monekosso, Dorothy et al.
Advances in Visual Computing: 14th International Symposium on Visual Computing, ISVC 2019, Lake Tahoe, NV, USA, October 7–9, 2019, Proceedings, Part I. ed. / George Bebis; Richard Boyle; Bahram Parvin; Darko Koracin; Daniela Ushizima; Sek Chai; Shinjiro Sueda; Xin Lin; Aidong Lu; Daniel Thalmann; Chaoli Wang; Panpan Wu. Vol. 11844 1. ed. Cham: Springer Nature, 2019. p. 181–193 (Lecture Notes in Computer Science).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Skin identification using deep convolutional neural network

AU - Maktab Dar Oghaz, Mahdi

AU - Argyriou, Vasileios

AU - Monekosso, Dorothy

AU - Remagnino, Paolo

N1 - Organising Body: International Symposium on Visual Computing (ISVC)

PY - 2019/10/21

Y1 - 2019/10/21

N2 - 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.

AB - 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.

KW - Computer science and informatics

KW - Convolutional Neural Networks

KW - Deep learning Segmentation

KW - Skin texture analysis

U2 - 10.1007/978-3-030-33720-9_14

DO - 10.1007/978-3-030-33720-9_14

M3 - Conference contribution

SN - 978-3-030-33719-3

VL - 11844

T3 - Lecture Notes in Computer Science

SP - 181

EP - 193

BT - Advances in Visual Computing

A2 - Bebis, George

A2 - Boyle, Richard

A2 - Parvin, Bahram

A2 - Koracin, Darko

A2 - Ushizima, Daniela

A2 - Chai, Sek

A2 - Sueda, Shinjiro

A2 - Lin, Xin

A2 - Lu, Aidong

A2 - Thalmann, Daniel

A2 - Wang, Chaoli

A2 - Wu, Panpan

PB - Springer Nature

CY - Cham

T2 - 14th International Symposium on Visual Computing, ISVC 2019

Y2 - 7 October 2019 through 9 October 2019

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Maktab Dar Oghaz M, Argyriou V, Monekosso D, Remagnino P. Skin identification using deep convolutional neural network. In Bebis G, Boyle R, Parvin B, Koracin D, Ushizima D, Chai S, Sueda S, Lin X, Lu A, Thalmann D, Wang C, Wu P, editors, Advances in Visual Computing: 14th International Symposium on Visual Computing, ISVC 2019, Lake Tahoe, NV, USA, October 7–9, 2019, Proceedings, Part I. 1 ed. Vol. 11844. Cham: Springer Nature. 2019. p. 181–193. (Lecture Notes in Computer Science). doi: 10.1007/978-3-030-33720-9_14

Skin identification using deep convolutional neural network

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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Cite this