Landmark-based Dataset Generation using Mediapipe

This paper focuses on creating structured landmarks-based feature extraction using MediPpipe. MediaPipe is an opensource framework for building pipelines to perform computer vision inference over arbitrary sensory data such as video or audio. Hand and facial expression recognition play a significant role in various domains like Human-computer interaction, assistive technology and emotion analysis. Traditional datasets primarily rely on raw images, which pose challenges in terms of computational complexity and privacy concerns. This paper represents a alternative approach for dataset creation by extracting structured landmarks-based representation for hand gestures and facial expressions using MediaPipe