Creating a Sri Lankan Micro-Emotion Dataset for a Robust Micro-Expression Recognition System

Abstract

In interpersonal communication, the human face provides important signals of a person’s emotional states and intentions. Furthermore, micro-emotions play a major role in understanding hidden intentions. In psychological aspects, detecting micro-emotions play a major role. In addition, lie detection, criminal identification, and security systems are other applications, where detecting micro-emotion accurately is essential. Revealing a micro-expression is quite difficult for humans because people tend to conceal their subtle emotions. As a result, training a human is expensive and time-consuming. Therefore, it is important to develop robust computer vision and machine learning methods to detect micro-emotions. Convolutional Neural Network (CNN) is the most used deep learning-based method in recent years. This research focuses on developing a 3D-CNN model to detect and classify Micro-emotions and creating a local Micro-emotion database. From the related research work we have considered this is the first attempt made at creating a Sri Lankan micro-emotion dataset. Having a local micro-emotion dataset is essential in formulating more accurate real-time applications focused on deep learning methods. Therefore, in this research, our main objective is to create a Sri Lankan micro-emotion database for future micro-emotion recognition and detection research.