Real Time Skin Color Detection Based on Adaptive HSV Thresholding

Authors

  • Mohammed Elamine Moumene Department of Mathematics and Computer Science, Faculty of Exact Sciences and Informatics, University Abdelhamid Ibn Badis Mostaganem, Algeria https://orcid.org/0000-0003-1031-8701
  • Khadidja Benkedadra Department of Mathematics and Computer Science, Faculty of Exact Sciences and Informatics, University Abdelhamid Ibn Badis Mostaganem, Algeria
  • Fatima Zohra Berras Department of Mathematics and Computer Science, Faculty of Exact Sciences and Informatics, University Abdelhamid Ibn Badis Mostaganem, Algeria

DOI:

https://doi.org/10.13052/jmm1550-4646.1867

Keywords:

Human Skin detection, Color Segmentation, Histogram Analysis, image thresholding

Abstract

The detection of human skin color has been studied extensively during the past two decades. It is an essential task for various computer vision applications such as biometric authentication, face/hands tracking and gesture analysis. New machine learning methods are effective for skin color detection. However, they are not suitable for real time applications since they are computationally heavy. A lightweight approach for skin color detection consists of using segmentation rules extracted by an investigation on skin color distribution. The kin appearance varies with diversity of image types, acquisition parameters and scene illumination. There are no general segmentation rules that provide effective skin segmentation for different scene conditions. In this paper we present a real-time skin color detector which adapts itself according to tracked human parts. First, initial thresholds are calculated using two popular skin datasets. Those thresholds can also be calculated quickly using small training sets. The proposed skin color detector showed comparable skin segmentation to DeepLabV3++ application and an improvement in term of F1 measure when compared to methods that relies on static rules.

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Author Biographies

Mohammed Elamine Moumene, Department of Mathematics and Computer Science, Faculty of Exact Sciences and Informatics, University Abdelhamid Ibn Badis Mostaganem, Algeria

Mohammed Elamine Moumene received a computer engineering degree from Mostaganem University in 2010 and a Ph.D. degree in computer vision from Oran 1 University in 2018. He is currently working as an assistant professor at the Department of Mathematics and Computer Science in Mostaganem university. His research areas include computer vision, machine learning and data mining.

Khadidja Benkedadra, Department of Mathematics and Computer Science, Faculty of Exact Sciences and Informatics, University Abdelhamid Ibn Badis Mostaganem, Algeria

Khadidja Benkedadra received a bachelor degree in Computer Systems from Mostaganem university in 2018, then a Master degree in Information Systems Engineering from the same university in 2020. She is currently working as the IT coordinator of a company and a freelance data analyst. She is specialized in image processing.

Fatima Zohra Berras, Department of Mathematics and Computer Science, Faculty of Exact Sciences and Informatics, University Abdelhamid Ibn Badis Mostaganem, Algeria

Fatima Zohra Berras received a bachelor’s degree in Computer Systems from Mostaganem University in 2018, then a Master’s degree in Information Systems Engineering from the same University in 2020. She is currently a student in Autonomic Systems at the University of Paris Saclay. She is specialized in image processing and machine learning.

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Published

2022-07-18

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Articles