How Effective Are Meta-heuristics for Recognising Hand Gestures

Abstract

Due to the gradient-free mechanism, flexibility, high local optima avoidance, and simplicity, meta-heuristics have been reliable alternatives to conventional optimisation techniques over the course of last two decades. This has resulted in the application of such techniques in diverse branches of science and technology. Despite all the successful applications, meta-heuristics are less effective in real-time applications where there is a need to find the optimal solutions instantly due to the need for a large number of function evaluations. This paper investigates the effectiveness of meta-heuristics in modelling hands for recognising hand gestures. Several well-known and recent algorithms have been utilised to find an optimal shape for a 3D model of the hand. Qualitative and quantitative results have been collected to see how well meta-heuristics perform in this field. Firstly, the results show that a free model of the hand can be very expensive to optimise: a constrained model is essential to reduce the search space. Secondly, the results show that population-based algorithms are more suitable rather than individual-based mainly because of the presence of a large number of local solutions. Thirdly, despite the accuracy of the optimal model obtained using population-based algorithms, the run time is an issue which should be considered. Finally, several recommendations are made for reducing the run time of meta-heuristics and making them more practical in the field of gesture detection.