Facial Skin Color Segmentation Using Otsu Thresholding Algorithm
DOI:
https://doi.org/10.33633/jais.v7i1.5513Abstract
The development of technology and information is currently very fast. One of the fields of technology and information that is experiencing development is the field of digital image processing. There are many technologies today that utilize digital images such as facial recognition, object detection and many others. Skin is one of the largest components of the human body. Currently, technology in the identification of skin color is widely used in recognizing the human race. In this study, skin color detection uses the YCbCr color space, which in this study only uses the range of Cb and Cr values, and ignores the Y value. Where Y is the lighting in the image. So if not changed, the image will contain light effects that can change the characteristics of skin color. However, problems were found because the detected images were not segmented properly, such as clothes and hair from the tested images were still detected as skin. Therefore, the HCbCr color space method is proposed where the Hue value will represent the color of visible light. While the Otsu Thresholding method will separate the background from the object in the digital image.References
D. Student et al., “URGENCY LEGAL ASPECTS OF GROWTH INFORMATION TECHNOLOGY IN INDONESIA Hardianto Djanggih,” no. 11.
S. Manoharan, “A SMART IMAGE PROCESSING ALGORITHM FOR TEXT RECOGNITION , INFORMATION EXTRACTION AND VOCALIZATION FOR THE VISUALLY CHALLENGED,” vol. 01, no. 01, pp. 31–38, 2019.
A. A. Ross, K. M. Müller, J. S. Weese, and J. D. Neufeld, “Comprehensive skin microbiome analysis reveals the uniqueness of human skin and evidence for phylosymbiosis within the class Mammalia,” vol. 115, no. 25, 2018, doi: 10.1073/pnas.1801302115.
M. M. T and O. M. E. E, “An improved human skin detection and localization by using machine learning techniques in RGB and YCbCr color spaces An Improved Human Skin Detection and Localization by using Machine Learning Techniques in RGB and YCbCr Color Spaces.”
S. Minaee, Y. Boykov, F. Porikli, A. Plaza, N. Kehtarnavaz, and D. Terzopoulos, “Image Segmentation Using Deep Learning : A Survey,” pp. 1–22.
O. A. Omer, “Optimizing Remote Photoplethysmography Using Adaptive Skin Segmentation for Real-Time Heart Rate Monitoring,” IEEE Access, vol. 7, pp. 76513–76528, 2019, doi: 10.1109/ACCESS.2019.2922304.
Y. U. N. Tan, J. Qin, X. Xiang, W. Ma, W. Pan, and N. N. Xiong, “A Robust Watermarking Scheme in YCbCr Color Space Based on Channel Coding,” IEEE Access, vol. 7, pp. 25026–25036, 2019, doi: 10.1109/ACCESS.2019.2896304.
H. Al Fatta, S. Pariyasto, and W. W. Widiyanto, “Prototype of Pornographic Image Detection with YCbCr and Color Space ( RGB ) Methods of Computer Vision,” 2019 Int. Conf. Inf. Commun. Technol., pp. 117–122, 2019.
Z. Tufail et al., “Improved Dark Channel Prior for Image Defogging Using RGB and YCbCr Color Space,” IEEE Access, vol. 6, pp. 32576–32587, 2018, doi: 10.1109/ACCESS.2018.2843261.
A. Akagic, E. Buza, S. Omanovic, and A. Karabegovic, “Pavement Crack Detection Using Otsu Thresholding for Image Segmentation.”
A. K. Khambampati, D. Liu, S. K. Konki, and K. Y. Kim, “An automatic detection of the ROI using Otsu thresholding in nonlinear difference EIT imaging,” vol. 1748, no. c, 2018, doi: 10.1109/JSEN.2018.2828312.
A. K. Bhandari, A novel beta differential evolution algorithm-based fast multilevel thresholding for color image segmentation, vol. 0123456789. Springer London, 2018.
A. K. Bhandari, S. Maurya, and A. K. Meena, “Social Spider Optimization Based Optimally Weighted Otsu Thresholding for Image Enhancement,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. PP, pp. 1–13, 2018, doi: 10.1109/JSTARS.2018.2870157.
I. Introduction, “Grayscale-based block scrambling image encryption using YCbCr color space for encryption-then-compression systems,” vol. 8, no. 2019, 2021, doi: 10.1017/ATSIP.2018.33.
M. H. Brain, P. Ayu, W. Purnama, and S. Arlis, “Image Segmentation of Acute Myeloid Leukemia Using Multi Otsu Thresholding Image Segmentation of Acute Myeloid Leukemia Using Multi Otsu Thresholding,” 2021, doi: 10.1088/1742-6596/1803/1/012016.
Z. Alqadi, “Performance Analysis of Artificial Neural Networks used for Color Image Recognition and Retrieving Performance Analysis of Artificial Neural Networks used for Color Image Recognition and Retrieving.”
Downloads
Published
Issue
Section
License
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).