Variability in gesticulation patterns: A robust framework for recognizing self co-articulated dynamic gestures
Dynamic hand gesture recognition has become an important research area in human–computer interaction, virtual reality, sign language interpretation, and intelligent surveillance systems. With the increasing demand for natural and contactless communication interfaces, gesture-based systems are gaining significant attention due to their intuitive and user-friendly nature. However, one of the major challenges in dynamic gesture recognition is inter-user variability, where differences in speed, style, and articulation patterns among users reduce the overall robustness and accuracy of recognition systems. Another critical issue is self co-articulation, which occurs when gestures overlap or influence each other during continuous motion, making feature extraction more complex. This study presents a dynamic hand gesture recognition system that addresses inter-user variability in gesticulation patterns. In our proposed system, a new set of features was employed, which divides the gesture into two halves, and feature extraction was performed after the removal of self-co-articulation. The efficiency of the proposed system was validated on a new set of gestures recorded in the LNM Institute of Information Technology Dynamic Hand Gesture Dataset-4, which consists of videos recorded according to different patterns. The performance of the proposed system was calculated with different features combined with individual as well as combinations of classifiers, such as support vector machine, k-nearest neighbor, naïve Bayes, adaptive neuro-fuzzy inference system, and discriminant analysis classifiers. The recognition accuracy of the naïve Bayes classifier was 93.13%, which is the best among all the classifiers. Recognition accuracy improved by about 10% with an increase in the number of features.
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