Chinese Face Dataset for Face Recognition in an Uncontrolled Classroom Environment

Since the position of the classroom surveillance camera is not fixed, the angle of the face captured through the surveillance video is also different. The deep learning-based face verification model has achieved good results in controlled environments, but there is still much room for improvement in the face verification ability in uncontrolled environments. The performance of the model depends not only on the results of the network but also on the quality and diversity of the dataset. The current Asian face dataset in an uncontrolled environment is insufficient; for this reason, this paper constructs a Chinese face dataset (UCEC-Face) in an uncontrolled classroom environment, which is collected by 35 real classroom surveillance videos. The UCEU dataset contains 7395 images of 130 subjects, including 44 males and 86 females. To verify that there is still room for improving the performance of existing face verification models for Asian face verification, we further utilize four models such as OpenFace and ArcFace for face verification, as well as the VGG-Face model for gender, expression, and age recognition on the UCEC-Face. The experimental results show that the UCEC-Face constructed in this paper is more challenging and difficult to verify in face verification tasks because it is closer to the real environment, and the best results obtained on the existing models only reach 69.7%, which is largely below the average accuracy of the identification results of other datasets.