Image Segmentation for Human Skin Detection
Human skin detection is the main task for various human–computer interaction applications. For this, several computer vision-based approaches have been developed in recent years. However, different events and features can interfere in the segmentation process, such as luminosity conditions, skin ton...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/23/12140 |
| _version_ | 1797463641392414720 |
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| author | Marcelo Leite Wemerson Delcio Parreira Anita Maria da Rocha Fernandes Valderi Reis Quietinho Leithardt |
| author_facet | Marcelo Leite Wemerson Delcio Parreira Anita Maria da Rocha Fernandes Valderi Reis Quietinho Leithardt |
| author_sort | Marcelo Leite |
| collection | DOAJ |
| description | Human skin detection is the main task for various human–computer interaction applications. For this, several computer vision-based approaches have been developed in recent years. However, different events and features can interfere in the segmentation process, such as luminosity conditions, skin tones, complex backgrounds, and image capture equipment. In digital imaging, skin segmentation methods can overcome these challenges or at least part of them. However, the images analyzed follow an application-specific pattern. In this paper, we present an approach that uses a set of methods to segment skin and non-skin pixels in images from uncontrolled or unknown environments. Our main result is the ability to segment skin and non-skin pixels in digital images from a non-restrained capture environment. Thus, it overcomes several challenges, such as lighting conditions, compression, and scene complexity. By applying a segmented image examination approach, we determine the proportion of skin pixels present in the image by considering only the objects of interest (i.e., the people). In addition, this segmented analysis can generate independent information regarding each part of the human body. The proposed solution produces a dataset composed of a combination of other datasets present in the literature, which enables the construction of a heterogeneous set of images. |
| first_indexed | 2024-03-09T17:53:38Z |
| format | Article |
| id | doaj.art-c35fdf25750046ea8886457904e396bd |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T17:53:38Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-c35fdf25750046ea8886457904e396bd2023-11-24T10:31:38ZengMDPI AGApplied Sciences2076-34172022-11-0112231214010.3390/app122312140Image Segmentation for Human Skin DetectionMarcelo Leite0Wemerson Delcio Parreira1Anita Maria da Rocha Fernandes2Valderi Reis Quietinho Leithardt3Master Program in Applied Computer Science, School of Sea, Science and Technology, University of Vale do Itajaí, Itajaí 88302-901, BrazilMaster Program in Applied Computer Science, School of Sea, Science and Technology, University of Vale do Itajaí, Itajaí 88302-901, BrazilMaster Program in Applied Computer Science, School of Sea, Science and Technology, University of Vale do Itajaí, Itajaí 88302-901, BrazilVALORIZA, Research Center for Endogenous Resources Valorization, Instituto Politécnico de Portalegre, 7300-555 Portalegre, PortugalHuman skin detection is the main task for various human–computer interaction applications. For this, several computer vision-based approaches have been developed in recent years. However, different events and features can interfere in the segmentation process, such as luminosity conditions, skin tones, complex backgrounds, and image capture equipment. In digital imaging, skin segmentation methods can overcome these challenges or at least part of them. However, the images analyzed follow an application-specific pattern. In this paper, we present an approach that uses a set of methods to segment skin and non-skin pixels in images from uncontrolled or unknown environments. Our main result is the ability to segment skin and non-skin pixels in digital images from a non-restrained capture environment. Thus, it overcomes several challenges, such as lighting conditions, compression, and scene complexity. By applying a segmented image examination approach, we determine the proportion of skin pixels present in the image by considering only the objects of interest (i.e., the people). In addition, this segmented analysis can generate independent information regarding each part of the human body. The proposed solution produces a dataset composed of a combination of other datasets present in the literature, which enables the construction of a heterogeneous set of images.https://www.mdpi.com/2076-3417/12/23/12140skin segmentationskin detectioncomputer visiondigital image processing |
| spellingShingle | Marcelo Leite Wemerson Delcio Parreira Anita Maria da Rocha Fernandes Valderi Reis Quietinho Leithardt Image Segmentation for Human Skin Detection Applied Sciences skin segmentation skin detection computer vision digital image processing |
| title | Image Segmentation for Human Skin Detection |
| title_full | Image Segmentation for Human Skin Detection |
| title_fullStr | Image Segmentation for Human Skin Detection |
| title_full_unstemmed | Image Segmentation for Human Skin Detection |
| title_short | Image Segmentation for Human Skin Detection |
| title_sort | image segmentation for human skin detection |
| topic | skin segmentation skin detection computer vision digital image processing |
| url | https://www.mdpi.com/2076-3417/12/23/12140 |
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