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2021 | OriginalPaper | Chapter

Facial Skin Type Classification Based on Microscopic Images Using Convolutional Neural Network (CNN)

Authors : Sofia Saidah, Yunendah Nur Fuadah, Fenty Alia, Nur Ibrahim, Rita Magdalena, Syamsul Rizal

Published in: Proceedings of the 1st International Conference on Electronics, Biomedical Engineering, and Health Informatics

Publisher: Springer Singapore

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Abstract

Skin is part of the human body that has a function as a barrier from the external environment and gives a physical appearance to an individual. In general, human skin types are classified into normal, dry, oily and combination skin. These skin types affected by the amount and change in facial sebum secretion and hydration ability. Determination of the type of facial skin is needed to determine skin care products and cosmetics in accordance with the type of facial skin they have. In this research, a system design for digital-based facial skin types using Convolutional Neural Network (CNN) method which has advantages to produce features and characteristics from the microscopic images dataset. The primary datasets taken directly using microscopic cameras and have been validated by a dermatologist. The CNN proposed model in this study consists of 3 hidden layers that use 3 × 3 size filters with output channels 8, 16 and 32 respectively, fully connected layer and softmax activation. The proposed model was able to classify the skin types into normal, dry, oily skin conditions and the combination with the best accuracy of 99.5% from 1200 training images and 400 test images used, meanwhile the parameters of recall precision and f-1 score produce values close to 1, which means that it is almost perfect or it can be said that the error is small.

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Literature
1.
go back to reference Chu DH (2012) Structure and development of the skin. in fitzpatrick’s dermatology in general medicine, 8th edn. In: Goldsmith LA, Katz SI, Gilchrest BA, Paller AS, Leffell DJ, Wolff K (eds) The McGraw-Hill Companies, Inc. pp 58–75 Chu DH (2012) Structure and development of the skin. in fitzpatrick’s dermatology in general medicine, 8th edn. In: Goldsmith LA, Katz SI, Gilchrest BA, Paller AS, Leffell DJ, Wolff K (eds) The McGraw-Hill Companies, Inc. pp 58–75
2.
go back to reference Youn SW (2015) Cosmetic facial skin types. In: P. H. et al (ed) Measuring the skin. Springer International Publishing Switzerland, pp 1–6 Youn SW (2015) Cosmetic facial skin types. In: P. H. et al (ed) Measuring the skin. Springer International Publishing Switzerland, pp 1–6
3.
go back to reference Choi CW, Choi JW, Youn SW (2013) Subjective facial skin type, based on the sebum related symptoms, can reflect the objective casual sebum level in acne patients. Ski Res Technol 19(2):176–182 Choi CW, Choi JW, Youn SW (2013) Subjective facial skin type, based on the sebum related symptoms, can reflect the objective casual sebum level in acne patients. Ski Res Technol 19(2):176–182
4.
go back to reference Baumann L (2015) Baumann skin type indicator—a novel approach to understanding skin type. In: Maibach H (ed) Handbook of cosmetic science and technology, pp 29–39 Baumann L (2015) Baumann skin type indicator—a novel approach to understanding skin type. In: Maibach H (ed) Handbook of cosmetic science and technology, pp 29–39
5.
go back to reference Crowther JM (2016) Method for quantification of oils and sebum levels on skin using the Sebumeter®. Int J Cosmet Sci 38(2):210–216 Crowther JM (2016) Method for quantification of oils and sebum levels on skin using the Sebumeter®. Int J Cosmet Sci 38(2):210–216
6.
go back to reference Indriyani I, Sudarma M (2020) Classification of facial skin type using discrete wavelet transform, contrast, local binary pattern and support vector machine. J Theor Appl Inf Technol 98(5):768–779 Indriyani I, Sudarma M (2020) Classification of facial skin type using discrete wavelet transform, contrast, local binary pattern and support vector machine. J Theor Appl Inf Technol 98(5):768–779
7.
go back to reference Neighbor DMK, Tritoasmoro II, Susatio E (2012) Klasifikasi Jenis Kulit Wajah Berdasarkan Analisis Tekstur Dengan Metode K-Nearest Neighbor, pp 0–6 Neighbor DMK, Tritoasmoro II, Susatio E (2012) Klasifikasi Jenis Kulit Wajah Berdasarkan Analisis Tekstur Dengan Metode K-Nearest Neighbor, pp 0–6
8.
go back to reference Farhan MR, Widodo AW, Rahman MA (2019) Ekstraksi Ciri Pada Klasifikasi Tipe Kulit Wajah Menggunakan Metode Haar Wavelet. J Pengemb Teknol Inf dan Ilmu Komput 3(3):2903–2909 Farhan MR, Widodo AW, Rahman MA (2019) Ekstraksi Ciri Pada Klasifikasi Tipe Kulit Wajah Menggunakan Metode Haar Wavelet. J Pengemb Teknol Inf dan Ilmu Komput 3(3):2903–2909
9.
go back to reference Firaz T, Nusantara B, Atmaja RD, Elektro FT, Telkom U (2018) Klasifikasi Jenis Kulit Wajah Pria Berdasarkan Tekstur Menggunakan Metode Gray Level Co-Occurrence Matrix (Glcm) Dan Support Vector Machine (Svm) Classification of Men’ S Face Skin Types Based the Texture Using Gray Level Co-Occurrence Matrix Glcm 5(2):2130–2137 Firaz T, Nusantara B, Atmaja RD, Elektro FT, Telkom U (2018) Klasifikasi Jenis Kulit Wajah Pria Berdasarkan Tekstur Menggunakan Metode Gray Level Co-Occurrence Matrix (Glcm) Dan Support Vector Machine (Svm) Classification of Men’ S Face Skin Types Based the Texture Using Gray Level Co-Occurrence Matrix Glcm 5(2):2130–2137
10.
go back to reference Nguyen LD, Lin D, Lin Z, Cao J (2018) Deep CNNs for microscopic image classification by exploiting transfer learning and feature concatenation. In: Proceedings of IEEE International Symposium on Circuits Systems 2018 May Nguyen LD, Lin D, Lin Z, Cao J (2018) Deep CNNs for microscopic image classification by exploiting transfer learning and feature concatenation. In: Proceedings of IEEE International Symposium on Circuits Systems 2018 May
11.
go back to reference Xue Y, Ray N (2018) Cell detection in microscopy images with deep convolutional neural network and compressed sensing, pp 1–29 Xue Y, Ray N (2018) Cell detection in microscopy images with deep convolutional neural network and compressed sensing, pp 1–29
12.
go back to reference Fukushima K (1980) Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern 36(4):193–202 Fukushima K (1980) Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern 36(4):193–202
13.
go back to reference Carruthers A, Carruthers J (2013) Handwritten digit recognition with a back-propagation network. Dermatol Surg 39(1 Pt 2):149 Carruthers A, Carruthers J (2013) Handwritten digit recognition with a back-propagation network. Dermatol Surg 39(1 Pt 2):149
Metadata
Title
Facial Skin Type Classification Based on Microscopic Images Using Convolutional Neural Network (CNN)
Authors
Sofia Saidah
Yunendah Nur Fuadah
Fenty Alia
Nur Ibrahim
Rita Magdalena
Syamsul Rizal
Copyright Year
2021
Publisher
Springer Singapore
DOI
https://doi.org/10.1007/978-981-33-6926-9_7