Face recognition via deep learning using data augmentation based on orthogonal experiments

Zhao Pei, Hang Xu, Yanning Zhang, Min Guo, Yang Yee-Hong

Research output: Contribution to journalArticlepeer-review

40 Scopus citations

Abstract

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%.

Original languageEnglish
Article number1088
JournalElectronics (Switzerland)
Volume8
Issue number10
DOIs
StatePublished - Oct 2019

Keywords

  • Class attendance
  • Data augmentation
  • Deep learning
  • Face recognition
  • Orthogonal experiments

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