Engineering Graduate Student’s class Attentiveness Based Smart Attendance Monitoring System using Multi-Layer Deep Neural Network
This paper proposes a novel method of students’ attendance monitoring system is based on the class attentiveness of individual student during the lecturing session. The class attentiveness of a student is monitored through various measures such as continuous watching of the student towards the faculty member lecture, mouth movements, eye lid status and yawning. The input to the process is a streaming video fed from the high resolution camera mounted in the classroom and output is an excel sheet at the end of each session with attendance of the students. Multi-layer layer deep neural network architecture with a sequence of convolutional layer, pooling layer and fully connected layer is employed in this system. The proposed method assures a better performance in terms of accuracy and able to handle streaming data efficiently.
Keywords— face detection, smart attendance, Deep neural network, attentiveness.