Enhancing Human-Computer Interaction Through Vision-Based Hand Gesture Recognition: An OpenCV and Keras Approach

Introduction

Hands move as we talk - this is non-verbal chat. For millions with disabilities, sign language hand moves matter most. But many deaf people struggle to communicate as most don't know sign lingo. Hands shape the world around us. Sign language hands share crucial messages. Yet deaf folk face major challenges talking to the greater public. Hands speak volumes without words spoken. Communicating through sign language can be challenging for those unfamiliar with its gestures. Gesture recognition and classification platforms help bridge this gap by translating these gestures. Two main approaches exist for classifying hand gestures: vision-based and sensor-based. The vision-based approach involves cameras capturing the pose and movement of your hand. Then, special algorithms process the recorded images - fascinating, isn't it? However, this method requires intensive number crunching and computations. Images or videos need major prep work. They must separate out features like color, pixel values, and the hand's shape. In contrast, surface electromyography (sEMG) plays a vital role in Muscle-Gesture Computer Interfaces. Assessing the precision and robustness of hand gesture recognition - a crucial task. However, evaluating the trustworthiness of these classifiers has been absent, to our knowledge. This could arise from a lack of consensus on defining model reliability in this field. Our research highlighted concerns about model reliability in sEMG-based hand gesture identification. By characterizing model reliability as the quality of its uncertainty measures, and providing an offline system to analyze it, we demonstrated that ECNN possesses excellent potential for classifying finger movements.

Over the course of several decades, gesture recognition (Rao, 2024) in computer science has undergone a constant state of development. Early advancements, mainly for experimental purposes, concentrated on simple hand-tracking devices starting in the 1960s. Video-based gesture recognition systems began to appear in the 1980s, albeit they were limited by computer power. Interest in sign language interpretation and virtual reality interfaces increased dramatically in the 1990s. But it wasn't until the 2000s—when machine learning techniques were incorporated and substantially improved accuracy and robustness—that gesture recognition saw a major breakthrough. With its depth-sensing technology, Microsoft's Kinect, which debuted in 2010, popularized gesture recognition, especially in the context of interactive and gaming media. Simultaneously, scientists started investigating multimodal methods that integrated vision, depth, and motion sensors. Real-time processing and complicated pattern recognition were made possible with the introduction of deep learning in the 2010s, which further transformed gesture recognition (Wu, 2024). Wider applications in a number of fields, such as smart devices, healthcare, automobile safety, and human-computer interaction, resulted from this. With the promise of providing immersive user experiences, gesture detection has more recently found its way into wearable technologies and augmented reality platforms. Future smooth human-machine contact will be made possible by ongoing research aiming to improve accuracy, adaptability to a variety of contexts, and integration with natural language comprehension.

The hand gesture recognition system suffers from several significant drawbacks. Firstly, its accuracy is consistently low, which undermines its practical usefulness. Secondly, the reliance on electromyography signals for dataset preparation introduces complexities and challenges, particularly concerning signal noise and variability. Lastly, the overall implementation process is notably complex, potentially hindering user adoption and development efforts. Addressing these issues is paramount for improving the system's effectiveness, reliability, and user-friendliness. Our proposal revolves around a robust deep learning model, centered on a convolutional neural network (CNN) (Rangdale, 2024) for automatic hand gesture detection. We utilized whole images, eliminating pre-processing needs. A diverse sample set spanned multiple image classes, ensuring comprehensive data coverage.