Feasibility study on evaluation of audience´s concentration in the classroom with deep convolutional neural networks

In this paper, we developed an estimation system for degree of audience´s concentration by estimating individual´s behavior with a deep learning approach. Our system firstly detects candidate location of audiences (CLAs) from the movie with Ada-boost classifier composed of Haar-like filters and their integration process. Then, each CLA is investigated to determine the target audience is “concentrated”, “not concentrated” or “no exist” with 5-layered deep convolutional neural networks (DCNN). We used a total of 13 movies of which 3 movies were used for training of DCNN and the remains for evaluation. Our system achieved audience detection performance of precision = 84.8% and recall = 61.8% and estimation accuracy of individual attention as 72.8%.