Emotion and Gesture detection

摘要

Abstract: Machine learning algorithms have removed the constraints of computer vision. Researchers and developers have developed new approaches for detecting emotions making it possible to predict the behaviour and consecutive actions of human beings. As machine learning methods make use of GPUs' massive computation capability, these models' image processing skills are well suited to real-world issues. Computer vision has moved from a niche field to a variety of other fields, including behavioural sciences. These algorithms or models are utilised in a wide range of real-world applications, including security, driver safety, autonomous cars, human-computer interaction, and healthcare. Due to the emergence of graphics processing units, which are hardware devices capable of doing millions of computations in seconds or minutes, these models are constantly changing. Technologies like augmented reality and virtual reality are also on the rise. Robotic vision and interactive robotic communication are two of their most intriguing uses. Both verbal and visual modalities can be used to identify human emotions. Facial expressions are an excellent way to determine a person's emotional state. This work describes a real-time strategy for emotion and gesture detection. The fundamental idea is to use the MediaPipe framework which is based on real-time deep learning, to generate critical points. Furthermore, A series of precisely constructed mesh generators and angular encoding modules are used to encode the generated key points. Finally, by assessing failure instances of existing models, we are evaluating the applicability of emotion and gesture detection from our model.We are using models such as Random Forest(RF),logistic regression(LR),Gradient Classifier(GR) and Ridge classifier(RC). Real-time inference and good prediction quality are demonstrated by the suggested system and architecture. Keywords: Body Landmarks, MediaPipe, Prediction, Accuracy, Real-time on-device Tracking, Recognition.