Abstract
One of the most significant current discussions in Computer Science is the skin recognition. Many papers have studied the skin detection using different techniques, artificial vision, machine learning, deep learning among others. Despite its long success, the skin recognition has several problems in use. However, there has been little discussion about generate a skin tonalities classification. The aim of this paper is to propose a system to skin tonalities through geographic distribution based on clustering algorithms, pattern recognition and fuzzy logic. This distribution gives us the opportunity to classify the skin tonalities. We can study each skin tonality for any applications as medical diagnosis, security. In the first stage, we use the RGB color model to training the system. Then, we tested the system with different color models. We use color model with the best result to propose geographic distribution based skin tonalities. The results show that is possible to generate a skin tonalities classification. The proposed system is using to skin recognition, showing interesting results under controlled and no controlled conditions.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Brancati N, De Pietro G, Frucci M, Gallo L (2017) Human skin detection through correlation rules between the YCb and YCr subspaces based on dynamic color clustering. Comput Vis Image Underst 155:33–42. https://doi.org/10.1016/j.cviu.2016.12.001(ISSN 10773142)
Caballero-Morales SO, Trujillo-Romero F (2013) 3D modeling of the Mexican sign language for a speech-to-sign language system. Comp y Sist México 17(4):593–608
Casati JPB, Moraes DR, Rodrigues ELL (2013) SFA: a human skin image database based on FERET and AR facial images. In: IX workshop de Visão Computacional, 2013, Rio de Janeiro. Anais do VIII workshop de Visão Computacional, 2013
Dass A (2019) Humanae [on line]. http://humanae.tumblr.com. Accessed 15 Jan 2019
Dhall A, Goecke R, Lucey S, Gedeon T (2011) Static facial expression in tough conditions: data, evaluation protocol and benchmark, IEEE ICCV BEFIT workshop 2011
Dhall A, Goecke R, Lucey S, Gedeon T (2012) Collecting large, richly annotated facial-expression databases from movies. IEEE Multimedia 19(3):34–41. https://doi.org/10.1109/MMUL.2012.26
Du SY, Liu ZG (2015) A comparative study of different color spaces in computer-vision-based flame detection. Multimed Tools Appl 75(17):10291–10310
Farberg AS, Rigel DS (2017) The importance of early recognition of skin cancer. Dermatol Clin 35(4):xv–xvi. https://doi.org/10.1016/j.det.2017.06.019(ISSN 07338635, ISBN 9780323546621)
FMD (2019) FMD [on line]. https://www.fashionmodeldirectory.com/brands/united-colors-ofbenetton/advertisements/2009/spring-summer/39428/cameron-russell-867738/. Accessed 15 Jan 2019
García-Lamont F, Cervantes J, Lopez-Chau A, Rodríguez L (2018) Segmentation of images by color features: a survey. Neurocomputing. https://doi.org/10.1016/j.neucom.2018.01.091
Hernandez-Matamoros A, Bonarini A, Escamilla-Hernandez E, Nakano-Miyatake M, Perez-Meana H (2016) Facial expression recognition with automatic segmentation of face regions using a fuzzy based classification approach. Knowl Based Syst 110:1–14
Huang GB, Ramesh M, Berg T, Learned-Miller E (2007) Labeled faces in the wild: a database for studying face recognition in unconstrained environments. University of Massachusetts, Amherst, technical report 07-49, 2007
Jablonski NG (2006) Skin: a natural history. University of California Press, Berkeley
Jablonski S (2018) Chapter 3—evolution of human skin color and vitamin D. In: Feldman D (ed) vitamin D, 4th edn. Academic Press, New York, pp 29–44. https://doi.org/10.1016/B978-0-12-809965-0.00003-3(9780128099650)
Jablonski NG, Chaplin G (2000) The evolution of skin coloration. J Hum Evol 39:57–106. https://doi.org/10.1371/journal.pbio.0000027(also reprinted in: Barsh GS (2003) What controls variation in human skin color? PLoS Biol 1(1):e27)
Jurmain R, Kilgore L, Trevathan W (2009) Essentials of physical anthropology. Wadsworth Publishing, Belmont (0495509396)
Mahmoodi MR, Sayedi SM, Karimi F (2017) Color-based skin segmentation in videos using a multi-step spatial method. Multimed Tools Appl 76(7):9785–9801. https://doi.org/10.1007/s11042-016-3579-8
Mirror UK (2019) Mirror [on line]. https://www.mirror.co.uk/sport/boxing/floyd-mayweather-vs-conor-mcgregor-11080113. Accessed 15 Jan 2019
Naji S, Jalab HA, Kareem SA (2018) A survey on skin detection in colored images. Artif Intell Rev. https://doi.org/10.1007/s10462-018-9664-9
Narayanamurthy V, Padmapriya P, Noorasafrin A, Pooja B, Hema K, Yuhainis Firus Khan A, Nithyakalyani K, Samsuri F (2018) Skin cancer detection using non-invasive techniques. RSC Adv 8:28095–28130. https://doi.org/10.1039/C8RA04164D
Park U, Tong Y, Jain AK (2010) Age-invariant face recognition. IEEE Trans Pattern Anal Mach Intell 32(5):947–954. https://doi.org/10.1109/TPAMI.2010.14
Phung SL, Bouzerdoum A, Chai D (2005) Skin segmentation using color pixel classification: analysis and comparison. IEEE Trans Pattern Anal Mach Intell 27(1):148–154
Pujol FA, Pujol M, Jimeno-Morenilla A, Pujol MJ (2017) Face detection based on skin color segmentation using fuzzy entropy. Entropy 19(1):26. https://doi.org/10.3390/e19010026
Renato B (1959) Le razze e i popoli della terra: Razze, popoli, e culture. 4 vols. Vol. 1, Le razze e i popoli della terra. Torino: Unione Tipografico-EditriceTorinese
Rimoin L, Altieri L, Craft N, Krasne S, Kellman PJ (2015) Training pattern recognition of skin lesion morphology, configuration, and distribution. J Am Acad Dermatol 72(3):489–495. https://doi.org/10.1016/j.jaad.2014.11.016(ISSN 01909622)
Sanderson C, Lovell BC (2009) Multi-region probabilistic histograms for robust and scalable identity inference. ICB 2009, LNCS 5558, pp 199-208
Seo HJ, Milanfar P (2011) Action recognition from one example. IEEE Trans Pattern Anal Mach Intell 33(5):867–882. https://doi.org/10.1109/TPAMI.2010.156
Singhal E, Tiwari S (2017) Skin cancer detection using artificial neural network. Int J Adv Res Comput Sci 6(1):149–157. https://doi.org/10.26483/ijarcs.v6i1.2402
The Karolinska, Directed Emotional Faces—KDEF (2013) CD ROM from Department of Clinical Neuroscience, Psychology section, Karolinska Institutet. ISBN 91-630-7164-9
Thomas N (1905) Hautfarbentafel. by von Luschan. Man 5:160
Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. Comput Vis Pattern Recogn 1:511–518
Von Luschan F, von Luschan E (1914) Anthropologische messungen an 95 Englandern. Z Ethnol 46:58–80
Yolland W, Tschandl P (2018) Multimodal skin lesion classification using deep learning. Exp Dermatol 27(11):09066705. https://doi.org/10.1111/exd.13777
Zuo H, Fan H, Blasch E, Ling H (2017) Combining convolutional and recurrent neural networks for human skin detection. IEEE Signal Process Lett 24(3):289–293. https://doi.org/10.1109/LSP.2017.2654803(ISBN 9780128099650)
Acknowledgements
The authors would like to thank to the National Science and Technology Council of Mexico for the financial support during the realization of this research. Andres Hernandez-Matamoros appreciates the support received from Diana Matamoros, Luis Hernandez, and Francisco Matamoros.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Hernandez-Matamoros, A., Fujita, H., Nakano-Miyatake, M. et al. Scheme fuzzy approach to classify skin tonalities through geographic distribution. J Ambient Intell Human Comput 11, 2859–2870 (2020). https://doi.org/10.1007/s12652-019-01400-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-019-01400-4