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Effective detection of exposed target regions based on deep learning from multimedia data

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Abstract

With the development of high-performance visual sensors, it has been very easy to obtain a variety of image data. Of these image data, human face regions contain personal information to distinguish one from the others. Therefore, it is important to accurately detect unhidden face regions from an input image. This paper proposes a method of robustly detecting human face regions from an input color image with the use of a deep learning algorithm, one of the machine learning algorithms. The proposed method first transforms the RGB color model of an input image to the YCbCr color model, and then removes other regions than face regions to segment skin regions with the use of the pre-learned elliptical skin color distribution model. Subsequently, a CNN model-based deep learning algorithm was applied to robustly detect human face regions from the detected skin regions in the previous step. As a result, the proposed method segments face regions more efficiently than an existing method. The face region detection method proposed in this paper is expected to be usefully applied to practical areas related to multimedia data processing, such as video surveillance, target blocking, image security, visual data analysis, and object recognition and tracking.

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2019R1F1A1056475).

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Authors and Affiliations

  1. Department of Software, Anyang University, 22, 37-Beongil, Samdeok-Ro, Manan-Gu, Anyang, 14028, Korea

    Seok-Woo Jang

  2. Liberal and Arts College, Anyang University, 22, 37-Beongil, Samdeok-Ro, Manan-Gu, Anyang, 14028, Korea

    Byeongtae Ahn

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  1. Seok-Woo Jang
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  2. Byeongtae Ahn
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Correspondence to Byeongtae Ahn.

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Jang, SW., Ahn, B. Effective detection of exposed target regions based on deep learning from multimedia data. Multimed Tools Appl 79, 16609–16625 (2020). https://doi.org/10.1007/s11042-019-07832-6

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  • DOI: https://doi.org/10.1007/s11042-019-07832-6

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