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Open AccessArticle

Face Recognition via Deep Learning Using Data Augmentation Based on Orthogonal Experiments

1
Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi’an 710119, China
2
School of Computer Science, Shaanxi Normal University, Xi’an 710119, China
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School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
4
Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(10), 1088; https://doi.org/10.3390/electronics8101088
Received: 31 August 2019 / Revised: 16 September 2019 / Accepted: 23 September 2019 / Published: 25 September 2019
(This article belongs to the Special Issue Face Recognition and Its Applications)
Class attendance is an important means in the management of university students. Using face recognition is one of the most effective techniques for taking daily class attendance. Recently, many face recognition algorithms via deep learning have achieved promising results with large-scale labeled samples. However, due to the difficulties of collecting samples, face recognition using convolutional neural networks (CNNs) for daily attendance taking remains a challenging problem. Data augmentation can enlarge the samples and has been applied to the small sample learning. In this paper, we address this problem using data augmentation through geometric transformation, image brightness changes, and the application of different filter operations. In addition, we determine the best data augmentation method based on orthogonal experiments. Finally, the performance of our attendance method is demonstrated in a real class. Compared with PCA and LBPH methods with data augmentation and VGG-16 network, the accuracy of our proposed method can achieve 86.3%. Additionally, after a period of collecting more data, the accuracy improves to 98.1%. View Full-Text
Keywords: face recognition; data augmentation; class attendance; deep learning; orthogonal experiments face recognition; data augmentation; class attendance; deep learning; orthogonal experiments
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Pei, Z.; Xu, H.; Zhang, Y.; Guo, M.; Yang, Y.-H. Face Recognition via Deep Learning Using Data Augmentation Based on Orthogonal Experiments. Electronics 2019, 8, 1088.

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