Abstract
In this paper a direct, pixel-based skin detection method is proposed and evaluated. Proposed approach discards any spatial information that can be found in digital image and focuses entirely on data-oriented analysis. To ensure the best perfomance two classifiers (Regularized Logistic Regression and Artificial Neural Network with Regularization trained with Backpropagation) were deeply examined, evaluated and compared for this task. The best model achieved the almost perfect accuracy and quality of classification on the used ‘Skin Segmentation Dataset’ provided for the UCI Machine Learning Repository with over 99% accuracy, precision, recall and specificity.
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Acknowledgements
This work was supported by Polish Ministry for Science and Higher Education under internal grant BKM/514/RAu1/2015/t-21 for Institute of Automatic Control, Silesian University of Technology, Gliwice, Poland.
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Binias, B., Frąckiewicz, M., Jaskot, K., Palus, H. (2018). Pixel Classification for Skin Detection in Color Images. In: Nawrat, A., Bereska, D., Jędrasiak, K. (eds) Advanced Technologies in Practical Applications for National Security. Studies in Systems, Decision and Control, vol 106. Springer, Cham. https://doi.org/10.1007/978-3-319-64674-9_6
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DOI: https://doi.org/10.1007/978-3-319-64674-9_6
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