Color space selection for human skin detection using color-texture features and neural networks

Al-Mohair, Hani K.; Mohamad–Saleh, Junita; Suandi, Shahrel Azmin

doi:10.1109/iccoins.2014.6868362

Cited by 18 publications

(7 citation statements)

References 17 publications

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“…Penelitian ini lebih fokus pada komparasi ruang warna yang digunakan, ada lima ruang warna yang digunakan sebagai atribut klasifikasi warna kulit yaitu, RGB, YCbCr, YIQ, YDbDr, dan CIE l*a*b. Dataset yang digunakan bersumber dari dua dataset publik yaitu CUE dengan total 100 citra wajah dan dataset COMPAC dengan 800 citra wajah dengan berbagai variasi ras. Komparasi dari klasifikasi menggunakan Neural Network ini pada lima ruang warna yang berbeda, ternyata yang menghasilkan akurasi tertinggi adalah ruang warna YIQ pada dua dataset tersebut, yaitu CUE (92,18%) dan COMPAC (89,22%) [6].…”

Section: Pendahuluanunclassified

Perbandingan Algoritma Pembelajaran Mesin untuk Klasifikasi Warna Kulit Berdasarkan Warna Piksel Citra

Ridla

2022

JUSIFOR

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Klasifikasi warna citra digital untuk mendeteksi warna kulit atau bukan kulit merupakan hal yang sangat penting dalam dunia Computer Vision. Hal ini sangat membantu berbagai aplikasi dalam kehidupan nyata, seperti aplikasi deteksi wajah, gerakan tangan, sistem keamanan, deteksi penyakit kulit, pemblokiran konten dewasa, dan lain sebagainya. Warna merupakan komponen yang paling utama dalam hal mendeteksi warna kulit pada citra, oleh karena itu pada penelitian ini akan dilakukan eksperimen klasifikasi warna kulit pada piksel sebuah citra menggunakan ruang warna yang berbeda beda diantaranya, RGB, HSV, YCbCr, dan SCgCr dengan memanfaatkan algoritma pembelajaran mesin, yaitu KNN, Naïve Bayes, dan Random Forest. Pada penelitian ini, data yang digunakan merupakan data public Skin Segmented yang bersumber dari UCI dengan ruang warna RGB dan transformasi ke ruang warna lainnya, hasil klasifikasi yang dilakukan ternyata menghasilkan performa yang sangat baik, untuk algoritma KNN dan Random Forest selalu mendapatkan tingkat akurasi di atas 95% di semua ruang warna tersebut.

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Section: Pendahuluanunclassified

Perbandingan Algoritma Pembelajaran Mesin untuk Klasifikasi Warna Kulit Berdasarkan Warna Piksel Citra

Ridla

2022

JUSIFOR

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“…Despite that, this paper evaluates skin type's challenges only. To review the work on neural network perspective, Hani K Al-Mohair et al [13] submitted an extensive study of current work neural network perspective based human skin color detection. The aim of this article is to show different algorithms that used artificial neural network systems to detect skin color, define their techniques and assess their features and achievement.…”

Section: Related Workmentioning

confidence: 99%

Statistical survey and comprehensive review on human skin detection

2021

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With the advancement of data society today, pictures have turned out to be increasingly imperative. Automatic detection of human skin has been an area of active research for the past few years. Human skin detection assumes a vital job in a wide scope of picture preparing applications going from face detection, steganography, face tracking, age detection, discover pornographic images, the discovery of skin diseases gesture analysis and substance based picture recovery frameworks and to different human PC association spaces. Detecting human skin in complex pictures have ended up being a difficult issue since skin shading can fluctuate drastically in its appearance because of numerous variables, for example, illumination, race, maturing, imaging conditions, and complex foundation. In this study, we will study and analyze skin researches, where we will treat the weakness of previous research of survey on human skin detection methods. The reason for this investigation is to give a state-of-the-art study on human skin molding and detection methods in 1998-2019 periods. Furthermore, this research presented the statistical study for each issue stated before. We finish up with a few ramifications for a future course. Study results will benefit all researchers who are interested in human skin detection topic.

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“…In order to ascertain the best color spaces [27] used in color, and color texture color feature separately. In the study, the use of a popular classifier known as Multilayer Perception artificial neural network (MLP) was used for comparative analysis.…”

Section: A Comparative Studymentioning

confidence: 99%

“…Examples include images obtained under low-light conditions, legacy grayscale images and videos, and near-infrared images [87]. Furthermore, for human observers it is often the visual texture of skin that is prominent in an image in which many situations exist for which color information is either not available or not reliable [27], [79], [88]. Examples include images obtained under low-light conditions, legacy grayscale images and videos, and near-infrared images.…”

Section: ) Challenges Related To Skin Detection Without Color Informationmentioning

confidence: 99%

The Landscape of Research on Skin Detectors: Coherent Taxonomy, Open Challenges, Motivations, Recommendations and Statistical Analysis, Future Directions

et al. 2019

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This paper review and analysis the literature on skin detector (SD), in order to establish the coherent taxonomy and figure out the gap on this pivotal research area. An extensive search is conducted to identify articles that deal with skin detection, skin segmentation, skin tone detector, and skin recognition issues, related techniques are reviewed comprehensively and a coherent taxonomy of these articles is established. ScienceDirect, IEEE Xplore, and Web of Science databases are checked for articles on skin detector. A total of 2803 papers are collected from 2007 to February 2018. The set comprised 173 articles. The largest portion of the papers (n = 158/173) = 91% belong to Development and Design, that is aimed to develop an approach for skin classifier into skin and non-skin. A sum total of (n = 5/173) = 3% of the papers belong to Evaluation and Framework, (n = 10/173) = 6% papers was categorized as Comparative Study. This paper discusses the open challenges, motivations and recommendations of the related works. Furthermore, state-of-the-art is a step to demonstrate the novelty of the presented study by conducted a statistical analysis for previous studies such as (Dataset, Color spaces, features, image type, and Classification techniques) as a future direction for other researchers who are interested in SD.

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Color space selection for human skin detection using color-texture features and neural networks

Cited by 18 publications

References 17 publications

Perbandingan Algoritma Pembelajaran Mesin untuk Klasifikasi Warna Kulit Berdasarkan Warna Piksel Citra

Perbandingan Algoritma Pembelajaran Mesin untuk Klasifikasi Warna Kulit Berdasarkan Warna Piksel Citra

Statistical survey and comprehensive review on human skin detection

The Landscape of Research on Skin Detectors: Coherent Taxonomy, Open Challenges, Motivations, Recommendations and Statistical Analysis, Future Directions

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