The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Human Skin Detection Using RGB, HSV and YCbCr Color Models
Open accessAbstract
Human Skin detection deals with the recognition of skin-colored pixels and regions in a given image.Skin color is often used in human skin detection because it is invariant to orientation and size and is fast to process.A new human skin detection algorithm is proposed in this paper.The three main parameters for recognizing a skin pixel are RGB (Red, Green, Blue), HSV (Hue, Saturation, Value) and YCbCr (Luminance, Chrominance) color models.The objective of proposed algorithm is to improve the recognition of skin pixels in given images.The algorithm not only considers individual ranges of the three color parameters but also takes into account combinational ranges which provide greater accuracy in recognizing the skin area in a given image.
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Citations
Cites
Statistical Color Models with Application to Skin Detection
Jones, M., & Rehg, J. M. (2002) · International Journal of Computer Vision · doi:10.1023/a:1013200319198 · Cited by 1,542
A Survey on Pixel-Based Skin Color Detection Techniques
Vezhnevets, V., Sazonov, V., & Andreeva, A. D. (2003) · Cited by 936
Statistical color models with application to skin detection
Jones, M., & Rehg, J. M. (2003) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1999.786951 · Cited by 491
Comparative Study of Skin Color Detection and Segmentation in HSV and YCbCr Color Space
Shaik, K. B., Ganesan, P., Kalist, V., Sathish, B. S., & Jenitha, J. M. M. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.07.362 · Cited by 346
Comparison of five color models in skin pixel classification
Zarit, B. D., Super, B. J., & Quek, F. K. H. (2003) · Analysis and Modeling of Faces and Gestures · doi:10.1109/ratfg.1999.799224 · Cited by 333
A novel skin color model in YCbCr color space and its application to human face detection
Phung, S. L., Bouzerdoum, A., & Chai, D. (2003) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2002.1038016 · Cited by 261
A Bayesian approach to skin color classification in YCbCr color space
Chai, D., & Bouzerdoum, A. (2002) · doi:10.1109/tencon.2000.888774 · Cited by 251
A comparative assessment of three approaches to pixel-level human skin-detection
Brand, J., & Mason, J. S. (2002) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2000.905653 · Cited by 227
Optimum color spaces for skin detection
Albiol, A., Torres, L., & Delp, E. J. (2002) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2001.958968 · Cited by 195
A Fusion Approach for Efficient Human Skin Detection
Tan, W. R., Chan, C. S., Yogarajah, P., & Condell, J. V. (2011) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2011.2172451 · Cited by 165
Research works printed but not linked: 1
- Poorani. M, Prathiba. T, Ravindran. G: "Integrated Feature Extraction for Image Retrieval", Interna- tional Journal of Computer Science and Mobile Computing vol.2 no. 2, 28-35(February2013)
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Metadata
- Authors
- Kolkur, S., Kalbande, D., Shimpi, P., Bapat, C., & Jatakia, J.
- Affiliations
- University of Mumbai (IN)
- Corresponding author
- Kolkur, S.
- ORCID
- Kalbande, D.
- Venue
- Proceedings of the International Conference on Communication and Signal Processing 2016 (ICCASP 2016)
- Publisher
- Atlantis Press
- Published
- 2017
- OpenAlex
- W2593035626
- Cited by
- 226
- References
- 12
- Citations by year
- 2026: 3 · 2025: 19 · 2024: 18 · 2023: 26 · 2022: 35 · 2021: 48 · 2020: 31 · 2019: 32 · 2018: 13 · 2017: 1
- FWCI
- 6.16
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc
- Locations
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Computer Science and Engineering
- Keywords
- human skin detection, skin color, RGB color model, HSV color space, YCbCr color space, color space comparison, skin region segmentation
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