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A novel approach for human skin detection using convolutional neural network

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Abstract

Human skin detection, which is one of the important pre-processing phases, has a wide range of applications such as face tracking, skin diseases, video surveillance, web content filtering, and so on. Skin detection is a challenging problem because skin color can vary dramatically in its appearance due to many factors such as illumination conditions, pose variations, race, aging, and complex background. Several methods dealing with skin detection assume that skin pixels can be extracted from background colors according to some thresholding rules related to a specific color model. Nevertheless, it is a complex task to recognize skin pixels under the challenging factors aforementioned. In the recent era, the success of deep convolutional neural network (CNN) has strongly influenced the field of computer vision. However, we could find only a few researches that apply deep learning methods to deal with the skin detection problem. This paper presents a novel approach based on CNN for skin detection. Extensive experiments show that the proposed approach exceeds the best result for other state-of-the-art methods.

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Authors and Affiliations

  1. National Engineering School of Sfax, University of Sfax, BP 1173, Sfax, Tunisia

    Khawla Ben Salah

  2. Research Lab: Math, Earth Sciences, Modeling and Intelligent Systems, Faculty of Sciences of Gafsa, University of Gafsa, Gafsa, Tunisia

    Khawla Ben Salah

  3. Faculty of Sciences of Gafsa, University of Gafsa, BP 2100, Gafsa, Tunisia

    Mohamed Othmani

  4. Faculty of Sciences of Sfax, University of Sfax, BP 1173, Sfax, Tunisia

    Monji Kherallah

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  1. Khawla Ben Salah
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  2. Mohamed Othmani
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  3. Monji Kherallah
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Correspondence to Khawla Ben Salah.

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Ben Salah, K., Othmani, M. & Kherallah, M. A novel approach for human skin detection using convolutional neural network. Vis Comput 38, 1833–1843 (2022). https://doi.org/10.1007/s00371-021-02108-3

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