Empowering facial emotion recognition in service industry - a two-stage convolutional neural network model

Deep-learning based facial emotion recognition (FER) has potential in the service industry. To solve conventional emotion recognition problems, this study proposes a deep learning-based model to achieve a highly-efficient FER system. The proposed FER model consists of two stages. The first stage uses the multitask convolution neural network to distinguish precise face-bounding box positions, whereas the second adopts a deep-learning network to achieve real-time recognition of emotions and features. By training our model using three global FER datasets, its accuracy indicates that the proposed model outperforms existing FER models. The study illustrated the model from three aspects. First, a massive facial database is investigated for model feasibility with a variety of service scenarios. Secondly, we demonstrated practical examples in the restaurant and retailing service industries. Third, the model performs advice by monitoring the emotion when the player is assembling Lego. The model can analyze human emotions in the service industry to identify customer satisfaction with products and/or services, fatigue in working domains, and/or safety in jobs. The model will help improve customer relationships, provide pleasant transaction solutions, and even help to broaden product offerings and promotions. Such facial recognition technology can further motivate new digital business models and change customer-server dynamics.