Real-time Multi-CNN-based Emotion Recognition System for Evaluating Museum Visitors’ Satisfaction
Do Hyung Kwon, Jeong Min Yu
Abstract
Conventional studies on the satisfaction of museum visitors focus on collecting information through surveys to provide a one-way service to visitors, and thus it is impossible to obtain feedback on the real-time satisfaction of visitors who are experiencing the museum exhibition program. In addition, museum practitioners lack research on automated ways to evaluate a produced content program's lifecycle and its appropriateness. To overcome these problems, we propose a novel multi-convolutional neural network, called VimoNet, which is able to recognize visitors emotions automatically in real-time based on their facial expressions and body gestures. Furthermore, we design a user preference model of content and a framework to obtain feedback on content improvement for providing personalized digital cultural heritage content to visitors. Specifically, we define seven emotions of visitors and build a dataset of visitor facial expressions and gestures with respect to the emotions. Using the dataset, we proceed with feature fusion of face and gesture images trained on the DenseNet-201 and VGG-16 models for generating a combined emotion recognition model. From the results of the experiment, VimoNet achieved a classification accuracy of 84.10%, providing 7.60% and 14.31% improvement, respectively, over a single face and body gesture-based method of emotion classification performance. It is thus possible to automatically capture the emotions of museum visitors via VimoNet, and we confirm its feasibility through a case study with respect to digital content of cultural heritage.