Abstract
Use of color spaces for human skin detection has been efficiently used for the several decades. It also plays an efficient role in face detection, tracking and recognition. This paper presents a comparative evaluation on the performance of skin color pixel classification methods using four rarely used color spaces and three commonly used skin detection methods. The first two skin detection methods used are piecewise linear decision boundary classifier algorithm and Gaussian color model which produce better results for color images with a wide variety of human skin tones. The third method deals with the Gaussian model with combination of two color spaces. All the results are experimentally evaluated with the help of few commonly used face databases.
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Subban, R., Mishra, R. (2014). Human Skin Segmentation in Color Images Using Gaussian Color Model. In: Thampi, S., Abraham, A., Pal, S., Rodriguez, J. (eds) Recent Advances in Intelligent Informatics. Advances in Intelligent Systems and Computing, vol 235. Springer, Cham. https://doi.org/10.1007/978-3-319-01778-5_2
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DOI: https://doi.org/10.1007/978-3-319-01778-5_2
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01777-8
Online ISBN: 978-3-319-01778-5
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