Abstract
Abstract Hand gesture recognition is becoming an increasingly integral part of our daily lives, enabling seamless communication, enhancing interaction, and revolutionizing multiple industries. To ensure a more precise and efficient system, the key aspect of hand gestures lies in detecting hand patterns and retrieving the hand gestures. However, as the volume of video data increases, extracting the essential hand patterns while excluding unnecessary frames becomes a challenge. Addressing this issue, a novel Harris Hawk Optimization K-Means frame reduction is proposed, inspired by the hunting behavior of Harris Hawks in nature. This proposed approach combines the Harris Hawk Optimization algorithm with the K-Means clustering method. The algorithm simulates the hunting behavior of Harris Hawks and utilizes Euclidean distance as a fitness function to determine the optimal frames. Subsequently, the K-Means clustering method is employed to group similar frames together based on these optimal selections. An average frame is generated and aggregated for each cluster to form a reduced set of frames. These reduced frames are then classified using the modified Mobilenet V2 model, outperforming other state-of-the-art techniques by achieving an exceptional accuracy rate of 99.93%. The experiment results lay the groundwork for incorporating the novel framework of hand gesture recognition into a range of applications, including sign language interpretation, human-computer interaction, and virtual reality systems.
Published Version
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