An enhanced artificial neural network for hand gesture recognition using multi-modal features
Shailaja Uke, Amol Zade
Abstract
The development of automatic application fields enhanced the advancement in Hand Gesture Recognition (HGR), which enhances human-computer interaction. In particular, hand gestures find a valuable application in hand games, virtual reality, and gesture-based signalling systems. Thus, detecting and recognising hand gestures gain technical significance. Although existing models highlight remarkable recognition outcomes, handling dynamic gestures is a challenging task. Hence, in this research, an enhanced flashfly optimisation-based artificial neural network is developed for the effective identification of hand gestures, and to overcome the existing challenges, the enhanced flash fly optimisation-based ANN technique is proposed. The main objective of the optimisation strategy is to effectively tune the parameters involved in the artificial neural network (ANN) by the attained optimal solution. The optimised ANN classifier is well trained by the pre-processed statistical and the Residual neural Networks (ResNet) features for consistently identifying hand movements. The attained performance of the enhanced flash fly optimisation-based ANN classifier during the representation of hand gestures based on the precision, recall, and F1 score is 0.940 %, 0.791 %, and 0.927 % for the training percentages 50 and 90. Hence, it is illustrated that the proposed HGR model exceeds the competent technique, demonstrating its effectiveness.