Abstract
Skin detection is an essential step in many human–machine interaction systems such as e-learning, security, communication… etc., it consists of extracting regions containing the skin in a digital image. This problem has become the subject of considerable research in the scientific community where a variety of approaches has been proposed in the literature; however, few recent reviews exist. Our principal goal in this paper is to extract skin regions using a Convolutional neural network called LeNet5. Our framework is divided into three main parts: At first, a deep learning is performed to Lenet5 network using 3354 positive examples and 5590 negative examples from SFA dataset, then and after a preprocessing of each arbitrary image the trained network will classify image pixels into skin/non-skin. Lastly, a thresholding and prost-processing of classified regions is carried out. The tests were carried out on images of variable complexity: indoor, outdoor, variable lighting, simple and complex background. The results obtained are very encouraging, we show the qualitative and quantitative results obtained on SFA and BAO datasets.
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Acknowledgements
The work described herein was partially supported by 8 Mai 1945 University and PRFU project through the grant number C00L07UN240120200001. The authors thank the staff of LAIG laboratory, who provided financial support.
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Bordjiba, Y., Bencheriet, C.E., Mabrek, Z. (2022). Skin Detection Based on Convolutional Neural Network. In: Ben Ahmed, M., Teodorescu, HN.L., Mazri, T., Subashini, P., Boudhir, A.A. (eds) Networking, Intelligent Systems and Security. Smart Innovation, Systems and Technologies, vol 237. Springer, Singapore. https://doi.org/10.1007/978-981-16-3637-0_6
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