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%.
Highlights
Taking class attendance in university classes is one of the commonly used methods to improve the performance of students studies in many universities
Model, which is based on the augmented training samples, our methods are compared with traditional face recognition methods such as Principal Component Analysis (PCA) and Local Binary Patterns Histograms (LBPH)
We propose a novel method for class attendance taking using a convolutional neural networks (CNNs)-based face recognition system
Summary
Taking class attendance in university classes is one of the commonly used methods to improve the performance of students studies in many universities. Another popular approach is roll call, by which the instructor records the attendance by calling the name of each student One advantage of these manual attendance-taking methods is that they require no special environment or equipment. There are three advantages of using face recognition in a class attendance-taking method. It reduces the burden for the instructor and the students. Training samples of faces of various poses, occlusion, and illumination are often required It is difficult, if not impossible, for the instructor to spend too much time taking photos during class. We compare our proposed class attendance-taking method with two typical face recognition algorithms, namely, Principal Component Analysis (PCA) and Local Binary Patterns Histograms (LBPH).
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