TY - JOUR
T1 - Face recognition via deep learning using data augmentation based on orthogonal experiments
AU - Pei, Zhao
AU - Xu, Hang
AU - Zhang, Yanning
AU - Guo, Min
AU - Yee-Hong, Yang
N1 - Publisher Copyright:
© 2019 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2019/10
Y1 - 2019/10
N2 - 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%.
AB - 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%.
KW - Class attendance
KW - Data augmentation
KW - Deep learning
KW - Face recognition
KW - Orthogonal experiments
UR - http://www.scopus.com/inward/record.url?scp=85073440403&partnerID=8YFLogxK
U2 - 10.3390/electronics8101088
DO - 10.3390/electronics8101088
M3 - 文章
AN - SCOPUS:85073440403
SN - 2079-9292
VL - 8
JO - Electronics (Switzerland)
JF - Electronics (Switzerland)
IS - 10
M1 - 1088
ER -