Convolutional neural networks for class attendance
Zhao Pei, Haixing Shang, Yi Su, Miao Ma, Yali Peng · 2017
Conventionally, students attendance records are taken manually by teachers through roll calling in the class. It is time-consuming and prone to errors. Moreover, records of attendance are difficult to handle and preserve for the long-term. In this paper, we propose a more conveniently method of attendance statistics, which achieved through the Convolutional Neural Network (CNN). The traditional method of face recognition, such as Eigenface, is sensitive to lighting, noise, gestures, expressions and etc. Hence, we utilize CNN to implement face recognition, in order to reduce the effect of environmental change on experimental results. In addition, CNN is a method which needs lots of data for training. To resolve the problem, we design a new method to collect face data which can get lots of face data quickly and conveniently.