Emotion Detection from Facial Expressions Using Augmented Reality

Emotion detection is a growing field of research in computer vision, in particular ARCore technology has opened new possibilities for real-time emotion detection using mobile devices. ARCore is a platform for building augmented reality experience on Android devices, which can detect the physical environment and provide accurate tracking and measurement of objects in real-time. In this paper, authors presented a novel approach to emotion detection using ARCore technology. The proposed system uses the camera and motion sensors of a mobile device to capture the user's facial expressions and movements and analyzes the data to detect emotions in real-time. Convolutional neural networks (CNNs) and long short-term memory (LSTM) networks were used in the design and implementation of a deep learning model to identify the user's emotions based on their facial expressions and body language. The proposed system has been evaluated on a dataset of facial expressions and achieved a high accuracy rate of 86.32%. The obtained results demonstrate the feasibility of using ARCore technology for emotion detection, which has potential applications in fields such as mental health, gaming, and human-computer interaction.