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Content loaded within last 14 days The Development of an Identification Photo Booth System based on a Deep Learning Automatic Image Capturing Method

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With advances in technology, photo booths equipped with automatic capturing systems have gradually replaced the identification (ID) photo service provided by photography studios, thereby enabling consumers to save a considerable amount of time and money. Common automatic capturing systems employ text and voice instructions to guide users in capturing their ID photos; however, the capturing results may not conform to ID photo specifications. To address this issue, this study proposes an ID photo capturing algorithm that can automatically detect facial contours and adjust the size of captured images. The authors adopted a deep learning method (You Only Look Once) to detect the face and applied a semi-automatic annotation technique of facial landmarks to find the lip and chin regions from the facial region. In the experiments, subjects were seated at various distances and heights for testing the performance of the proposed algorithm. The experimental results show that the proposed algorithm can effectively and accurately capture ID photos that satisfy the required specifications.
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Document Type: Research Article

Affiliations: 1: Department of Computer Science and Information Engineering, National Quemoy University, Taiwan 2: Department of Electrical Engineering, National Taiwan Ocean University, Taiwan 3: Department of Computer Science and Engineering, National Taiwan Ocean University, Taiwan

Publication date: March 1, 2021

This article was made available online on November 13, 2020 as a Fast Track article with title: "The Development of an Identification Photo Booth System based on a Deep Learning Automatic Image Capturing Method ".

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  • The Journal of Imaging Science and Technology (JIST) is dedicated to the advancement of imaging science knowledge, the practical applications of such knowledge, and how imaging science relates to other fields of study. The pages of this journal are open to reports of new theoretical or experimental results, and to comprehensive reviews. Only original manuscripts that have not been previously published, nor currently submitted for publication elsewhere, should be submitted.

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    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.

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