Skin identification using deep convolutional neural network

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

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

Skin identification using deep convolutional neural network

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

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	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. Organising Body: International Symposium on Visual Computing (ISVC) Organising Body: International Symposium on Visual Computing (ISVC)
DOIs	https://doi.org/10.1007/978-3-030-33720-9_14
Publication status	Published - Oct 2019

Bibliographical note

Note: 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.

Organising Body: International Symposium on Visual Computing (ISVC)

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

https://doi.org/10.1007/978-3-030-33720-9_14

1 Paper

Skin identification using deep convolutional neural network
Oghaz, M. M. D., Argyriou, V., Monekosso, D. & Remagnino, P., Oct 2019.
Research output: Contribution to conference › Paper › peer-review

Cite this

Argyriou, V., Monekosso, D., Remagnino, P., & Maktab Dar Oghaz, M. (2019). Skin identification using deep convolutional neural network. In 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. Organising Body: International Symposium on Visual Computing (ISVC) Organising Body: International Symposium on Visual Computing (ISVC) https://doi.org/10.1007/978-3-030-33720-9_14

Argyriou, Vasileios ; Monekosso, Dorothy ; Remagnino, Paolo et al. / Skin identification using deep convolutional neural network. 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. Organising Body: International Symposium on Visual Computing (ISVC) Organising Body: International Symposium on Visual Computing (ISVC). 2019.

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

note = "Note: 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. Organising Body: International Symposium on Visual Computing (ISVC) Organising Body: International Symposium on Visual Computing (ISVC)",

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language = "English",

booktitle = "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. Organising Body: International Symposium on Visual Computing (ISVC) Organising Body: International Symposium on Visual Computing (ISVC)",

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Argyriou, V, Monekosso, D, Remagnino, P & Maktab Dar Oghaz, M 2019, Skin identification using deep convolutional neural network. in 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. Organising Body: International Symposium on Visual Computing (ISVC) Organising Body: International Symposium on Visual Computing (ISVC). https://doi.org/10.1007/978-3-030-33720-9_14

Skin identification using deep convolutional neural network. / Argyriou, Vasileios; Monekosso, Dorothy; Remagnino, Paolo et al.
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. Organising Body: International Symposium on Visual Computing (ISVC) Organising Body: International Symposium on Visual Computing (ISVC). 2019.

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

TY - GEN

T1 - Skin identification using deep convolutional neural network

AU - Argyriou, Vasileios

AU - Monekosso, Dorothy

AU - Remagnino, Paolo

AU - Maktab Dar Oghaz, Mahdi

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

PY - 2019/10

Y1 - 2019/10

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

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

ER -

Argyriou V, Monekosso D, Remagnino P, Maktab Dar Oghaz M. Skin identification using deep convolutional neural network. In 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. Organising Body: International Symposium on Visual Computing (ISVC) Organising Body: International Symposium on Visual Computing (ISVC). 2019 doi: 10.1007/978-3-030-33720-9_14

Skin identification using deep convolutional neural network

Abstract

Bibliographical note

Keywords

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Research output

Skin identification using deep convolutional neural network

Cite this