2017 Volume 29 Issue 1 Pages 517-526
Faculty development (FD) program has been widely conducted in response to social concern in quality of higher education. We believe that analyzing of audiences’ state with engineering approaches will provide an applicative way to make a feedback to teachers. Video analysis takes advantages in applicability to practical due to their low cost and small requirements. Conventional methodologies, however, did not identify and/or implement essential image features to analyze audiences’ state under various conditions and therefore the availability of them was limited. In this study, we apply the pattern recognition framework for this task and propose an estimation system of state of audiences with convolutional neural networks (CNN), which can obtain effective image features through their learning process. Then, we focus on major issues pertaining to the exploration of CNN and assess the obtained image features. Our proposed system achieved audience detection performance of precision = 84.8% and recall = 61.8% and state estimation accuracy = 72.8% under various conditions. In addition, CNN showed good performance for this task under the situation that only limited numbers of training data were given. We confirmed from these results that CNN obtained essential image features for estimating state of audiences appropriately.
