An Improved Probability Density Function (PDF) for Face Skin Detection

Authors

  • Iptehaj Alhakam Computer Science Department, College of Education for Pure Science / Ibn Al-Haitham, University of Baghdad, Iraq
  • Nassir H Salman Computer Science Department, College of Science, University of Baghdad, Iraq

DOI:

https://doi.org/10.24996/ijs.2022.63.10.31

Keywords:

Skin color, HSV, PDF, Extreme value distribution function, Exponential distribution function, Face detection

Abstract

      Face Detection by skin color in the field of computer vision is a difficult challenge. Detection of human skin focuses on the identification of pixels and skin-colored areas of a given picture. Since skin colors are invariant in orientation and size and rapid to process, they are used in the identification of human skin. In addition features like ethnicity, sensor, optics and lighting conditions that are different are sensitive factors for the relationship between surface colors and lighting (an issue that is strongly related to color stability). This paper presents a new technique for face detection based on human skin. Three methods of Probability Density Function (PDF) were applied to detect the face by skin color; these are the Extreme Value Distribution Function and the Exponential Distribution Function methods, in addition to a new proposed model, over the HSV (Hue, Saturation, and Value) color space. The suggested technique aims to enhance skin pixel detection and improve the detection accuracy of a colored region in the human skin in a specific photo. The new model has proved to be 96.05% more accurate than the Extreme value distribution function and Exponential distribution function according to the selected region of the face during experiments. The images used in this paper were 380 color images from CalTech (California Technology Institute) dataset.

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Published

2022-10-30

How to Cite

Alhakam, I., & Salman, N. H. (2022). An Improved Probability Density Function (PDF) for Face Skin Detection. Iraqi Journal of Science, 63(10), 4460–4473. https://doi.org/10.24996/ijs.2022.63.10.31

Issue

Section

Computer Science