Enhancing the Performance Accuracy of Student Monitoring System by Face Detection and Tracking in Classroom Environment using RNN over MTCNN Algorithms

To enhance the accuracy of face recognition in a classroom environment using Recurrent Neural Network (RNN). The study consists of 2 groups i.e Recurrent Neural Network and Multi Cascaded Convolutional Neural Network (MTCNN) with sample size of 10 for each group. Gpower software is used to determine sample size with power value 0.8 and alpha is 0.05. Proposed model has an improved accuracy of 95.44% in facial detection and tracking than existing model of 91.73% with significance value of 0.97 (p¿0.05). End result of proposed model Recurrent neural network provides better accuracy when compared with Multi cascaded convolutional neural network.