Presented at the 3rd International Symposium on Innovative Approaches in Scientific Studies (Engineering and Natural Sciences) (ISAS2019-ENS), Ankara, Turkey, Apr 19, 2019

SETSCI Conference Proceedings, 2019, 4, Page (s): 504-507 , https://doi.org/

Published Date: 01 June 2019    | 681     6

Digital image processing has become popular research area recently because of its widespread application compatibility such as security, robotics, quality control, face authentication. Skin segmentation methodology, which is a tool of digital image processing, is useful in nudity alerts and face detection with its flexibility and simplicity. The purpose of the study is to develop general and simple model for skin segmentation with Extreme Learning Machine (ELM). ELM model is applied over Skin Segmentation Database from UCI Learning Repository. The reason for preference of UCI Database is its extensive samples from different age and race group of people.

Keywords - Skin Segmentation, Extreme Learning Machine, Machine Learning, Classification, Image Processing

[1] D. Chai and K. N. Ngan, "Face segmentation using skin color map in videophone applications," IEEE Trans. CSVr, vol. 9.4, pp. 551-564, Jun. 1999.

[2] R.L. Hsu, M. Abdel-Mottaleb, and A. K. Jain, "Face detection in color images," IEEE Trans. PAMI, vol. 24.5, pp.696-796, May 2002.

[3] A.O. Bălan, and M.J. Black, "The naked truth: Estimating body shape under clothing," in European Conference on Computer Vision. Springer, Berlin, Heidelberg, 2008, pp. 15-29.

[4] X.Z.J.Y.A. Waibel, "Segmenting hands of arbitrary color," in IEEE the Fourth IEEE International Conference on Automatic Face and Gesture Recognition (FG'000), 2000, vol. 446, p.453.

[5] S.L Phung, A. Bouzerdoum, and D. Chai. "Skin segmentation using color and edge information," in Seventh International Symposium on Signal Processing and Its Applications, Proceedings, 2003, vol. 1, pp. 525-528

[6] L. Sigal, S. Sclaroff, and V. Athitsos. "Estimation and prediction of evolving color distributions for skin segmentation under varying illumination," in Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No. PR00662), 2000, vol. 2, pp. 152-159.

[7] J.P.B. Casati, D.R. Moraes, and E.L.L. Rodrigues, "SFA: A human skin image database based on FERET and AR facial images," in IX workshop de Visao Computational, Rio de Janeiro, 2013.

[8] A. Uçar, "Color face recognition based on steerable pyramid transform and extreme learning machines," The Scientific World Journal 2014, 2014.

[9] H.K. Al-Mohair, J.M. Saleh, and S.A. Suandi, "Hybrid human skin detection using neural network and k-means clustering technique," Applied Soft Computing, vol. 33, pp. 337-347, 2015.

[10] S.M. Jaisakthi, and S. Mohanavalli, "Skin Segmentation using Ensemble Technique," Research Journal of Applied Sciences, Engineering and Technology, vol. 9.11, pp. 963-968, 2015.

[11] Y. Lei, W. Yuan, H. Wang, Y. Wenhu and W.Bo, "A skin segmentation algorithm based on stacked autoencoders," IEEE Transactions on Multimedia, vol. 19.4, pp. 740-749, 2017.

[12] S. Kolkur, D. Kalbande, P. Shimpi, C. Bapat and J. Jatakia, "Human skin detection using RGB, HSV and YCbCr color models," arXiv preprint arXiv:1708.02694, vol. 137, pp. 324-332, 2017.

[13] T. Dastane, V. Rao, K. Shenoy and D. Vyavaharkar, "An Effective Pixel-Wise Approach for Skin Colour Segmentation-Using Pixel Neighbourhood Technique," International Journal on Recent and Innovation Trends in Computing and Communication, vol. 6.3, pp. 182-186, 2018.

[14] E.C. Kiziltas, A. Uzun, and E. Yılmaz, "Skin Segmentation by Using Complex Valued Neural Network with HSV Color Spaces," International Journal of Multidisciplinary Studies and Innovative Technologies, vol. 3.1, pp. 1-4, 2019.

[15] G.B. Huang, Q.Y. Zhu and C.K. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, vol. 70.1-3, pp. 489-501, 2006.

[16] G.B. Huang H. Zhou, X. Ding, and R. Zhang, “Extreme learning machine for regression and multiclass classification,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 42.2, pp. 513-529, 2012.

[17] G.B. Huang, “An insight into extreme learning machines: random neurons, random features and kernels,” Cognitive Computation, vol.6.3, pp. 376-390, 2014.

[18] G. Huang, G.B. Huang, S. Song, and K. You, “Trends in extreme learning machines: A review,” Neural Networks, vol. 61, pp. 32-48, 2015.

[19] X. Deng, Q. Liu, Y. Deng and S. Mahadevan, "An improved method to construct basic probability assignment based on the confusion matrix for classification problem," Information Sciences, vol. 340, pp. 250-261, 2016.

[20] Yılmaz, E., “An expert system based on Fisher score and LS-SVM for cardiac arrhythmia diagnosis.” Computational and mathematical methods in medicine, 2013.

[21] P. Refailzadeh, L. Tang and H. Liu, Cross-validation, Encyclopedia of data base systems New York: Springer, pp. 532–538, 2009

[22] E. Yılmaz, “Fetal State Assessment from Cardiotocogram Data Using Artificial Neural Networks,” Journal of Medical and Biological Engineering, vol. 36.6, pp. 820-832, 2016.