Color Space to Detect Skin Image: The Procedure and Implication

Sukmawati Nur Endah, Retno Kusumaningrum, Helmie Arif Wibawa

Abstract


Skin detection is one of the processes to detect the presence of pornographic elements in an image. The most suitable feature for skin detection is the color feature. To be able to represent the skin color properly, it is needed to be processed in the appropriate color space. This study examines some color spaces to determine the most appropriate color space in detecting skin color. The color spaces in this case are RGB, HSV, HSL, YIQ, YUV, YCbCr, YPbPr, YDbDr, CIE XYZ, CIE L*a*b*, CIE L*u* v*, and CIE L*ch. Based on the test results using 400 image data consisting of 200 skin images and 200 non-skin images, it is obtained that the most appropriate color space to detect the color is CIE L*u*v*.


Full Text:

PDF

References


Asmare, M.H., Asirvadam, W.S, Iznito, L. 2009. Color Space Selection for

Color Image Enhancement Applications. Proceeding of International

Conference on Signal Acquisition and Processing (ICSAP) 2009.

KualaLumpur, Malaysia, 3-5 April 2009

Vasthi, P. I., & Kusumaningrum, R. 2015. Object segmentation for fruit

images using OHTA colour space and cascade threshold. In Science in

Information Technology (ICSITech), 2015 International Conference on (pp.

-325). IEEE.

Isa, S.M., Mariana, F. 2009.Deteksi Citra Pornografi Menggunakan TSL Color

Space dan Nudity Detection Algorithm. Prosiding. Seminar Nasional

Informatika 2009 UPN Veteran Yogyakarta. Yogyakarta, Indonesia, 23 Mei

Zhuo, L., Geng, Z., Zhang, J., & guang Li, X. 2016. ORB feature based web

pornographic image recognition. Neurocomputing, 173(3), 511-517.

Stricker, M., Orengo, M. 1995. Similarityof Color Images. Proceedings SPIE

SPIE's Symposium on Electronic Imaging: Science and Technology. San

Jose, CA, United, March 23.

Rapid Tables.2017. RGB to HSV Color Conversion. (Online) (http://www.

rapidtables.com/convert/color/rgb-to-hsv.htm, diakses 19 Agustus 2017)

EEMBC. 2006. RGB to YIQ Conversion. (Online) (https://www.eembc.org/

techlit/datasheets/yiq_consumer.pdf, diakses 19 Agustus 2017)

Schweyer, M. 2015. Color Corversion. (Online) (http://www.equasys.

decolorcon version.html, diakses 19 Agustus 2017)

Shi, Y. Q., Sun, H. 2000. Image and Video Compression for Multimedia

Engineering. CRC Press, United States of America

Ford, A., Roberts, A. 1998.Colour Space Conversions. (Online) (http://www.

poynton.com/PDFs/coloureq.pdf, di akses 8 September 2017)

Endah, S. N., & KN, D. M. 2012. Klasifikasi Ucapan Kata dengan Support

Vector Machine. Jurnal Masyarakat Informatika, 3(6), 7-14.




DOI: https://doi.org/10.15294/sji.v4i2.12013

Refbacks

  • There are currently no refbacks.




Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.