A deep learning based approach for student attendance recognition
Abstract
Traditional attendance recognition methods- such as using cards or signing into lists are susceptible to fraud- often consumes considerable time and effort in managing and processing information. With advancing technology and increasing security requirements, modern attendance recognition methods offer higher accuracy and convenience for organizations. This paper proposes an approach utilizing DeepFace technology and the deep learning architectures (MobileNet, ResNet) to face detection, face recognition and face anti-spoofing. The proposed approach is experimented on the existing dataset within the research community (CelebA-spoof) and on a dataset (named CUSC-Student) built by ourselves; and is evaluated by two metrics- Accuracy and F1-Score. From the experimental results, this approach also identifies the suitable model that achieves the high accuracy while requiring less computing resources. Therefore, it can be applied in practice, specifically for recognizing student attendance at our division.
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