Abstract
Nowadays, face recognition is applied in a wide range of applications. In this research, we apply face recognition to build the student attendance system using advanced deep learning method. The shortcomings of previous process are too many redundant steps, time consuming and depend heavily on human works; this leads to congestion and makes it inconvenient for both users and managers. To address those issues, we use our own deep learning method-convolutional neural network (CNN), a method used in many studies on computer vision. Based on the evaluation of the standard dataset ORL (by AT&T Research Lab) with different methods PCA-NN, LDA-DNN, the proposed method is introduced to combine with Haar Cascade in face detection to achieve the best result. Following that result, we then build an application and apply it in System Administration course of D18HT02 class, Thu Dau Mot University. The results obtained from lecturers who conducted the experiment reveal the promising capability of the proposed method.
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Keras: https://keras.io/
Tensorflow: https://www.tensorflow.org/
Opencv: https://opencv.org/
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Hung, B.T., Khang, N.N. (2021). Student Attendance System Using Face Recognition. In: Singh Mer, K.K., Semwal, V.B., Bijalwan, V., Crespo, R.G. (eds) Proceedings of Integrated Intelligence Enable Networks and Computing. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-6307-6_98
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DOI: https://doi.org/10.1007/978-981-33-6307-6_98
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