Automatic Face Recognition-Based Attendance System using 2D Convolutional Neural Network

Monitoring attendance is a crucial component in identifying a person’s presence in a corporate or educational setting. The modern method of recording attendance without the possibility of proxies can be accomplished simply by recognizing a person’s face. Hence, face recognition of an individual can make the laborious work of manually recording attendance easier. Convolutional neural network is a deep learning-based ok method, which is used to run the integrated attendance system. The real-time capture of the image allows for the recognition of a person’s face. After the dataset has been collected, the features are retrieved as NumPy arrays. The model’s primary goal is to identify various faces and log the desired attendance. A file with the extension of ‘*.txt’ contains a record of every person’s attendance. Since it makes it simpler to categorize the attendance daily, the file name is preserved as the date on which the attendance was recorded. At one moment, it is possible to recognize the faces of several people and store the names in a specific text file. In the work, authors discussed the above strategies to implement the model.