An Algorithm of Recommending Apposite ID Photos

Xuanang Feng, Miki Miyachi, Ziqi Zhu and Eisuke Kita ()
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Xuanang Feng: Nagoya University
Miki Miyachi: Nagoya University
Ziqi Zhu: Nagoya University
Eisuke Kita: Nagoya University

The Review of Socionetwork Strategies, 2020, vol. 14, issue 1, 109-121

Abstract: Abstract This article proposes an algorithm to recommend apposite ID photos for users by judging the photo of which the facial expression is apposite or not as the ID photo. Microsoft’s Kinect sensor is used for taking photos. Parts of the face, such as eyes, nose, and mouth, are analyzed as explanatory variables for judging face expression. Some body coordinate information such as head and shoulders is used to trim the photos. Neural networks and support vector machines are employed and compared to our proposed method. To achieve accurate results, ten examinees including specialized staff are selected for taking ID photo used for training models. A series of experiments are conducted to examine the validity. As a result, the accuracy of neural networks is better than that of the support vector machine. Furthermore, we analyze and discuss the difference between system results and specialized staffs’ opinions.

Keywords: Facial expression; ID photo; Neural network; Support vector machine (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s12626-019-00059-9

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