Application of Face Data Augmentation Based on Rotate-and-Render-DCGAN in Campus Security
Kai Wu, Ynanchao Yu, Xuerui Zhang, Jie Li, Qian Zhang · 2020 IEEE 3rd International Conference of Safe Production and Informatization (IICSPI) · 2020
Targeting at solving the problem of low accuracy of existing recognition methods caused by the imbalance of face data sets in the university security system, an unbalanced Data Augmentation method based on Rotate-and-Render-DCGAN is proposed to improve face recognition accuracy campus. Our goal is to correct the angled faces in the data set through Rotate-and-Render and generate artificial images to enrich the image database combined with DCGAN to improve the classifier's performance and the accuracy of face recognition. We evaluate and compare the impact of traditional Data Augmentation and Rotate-and-Render- DCGAN Data Augmentation on face recognition accuracy. The results show that the improvement effect of this method is more significant.