Hand gesture recognition based on a Harris Hawks optimized Convolution Neural Network
Gadekallu, Thippa Reddy; Srivastava, Gautam; Liyanage, Madhusanka; M., Iyapparaja; Chowdhary, Chiranji Lal; Koppu, Srinivas; Maddikunta, Praveen Kumar Reddy (2022-03-04)
Gadekallu, T. R., Srivastava, G., Liyanage, M., M., I., Chowdhary, C. L., Koppu, S., & Maddikunta, P. K. R. (2022). Hand gesture recognition based on a harris hawks optimized convolution neural network. Computers and Electrical Engineering, 100, 107836. https://doi.org/10.1016/j.compeleceng.2022.107836
© 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
https://creativecommons.org/licenses/by-nc-nd/4.0/
https://urn.fi/URN:NBN:fi-fe202301122582
Tiivistelmä
Abstract
Hand gestures are an effective method of communication, especially when we are communicating with people who cannot understand our spoken language. Furthermore, it is a key aspect to human–computer interaction. Understanding hand gestures is very important to ensure that listeners understand what speakers are attempting to communicate. Even though several researchers have proposed deep learning-based models for hand gesture recognition, the hyper-parameter tuning of these models is a relatively unexplored area. In this work, Convolutional Neural Networks (CNN) are used to classify hand gesture images. To tune the hyper-parameters of the CNN, a recently developed metaheuristic algorithm, namely, the Harris Hawks Optimization (HHO) algorithm, is used. Our in-depth comparative analysis proves that the proposed HHO-CNN hybrid model outperforms the existing models by attaining an Accuracy of 100%.
Kokoelmat
- Avoin saatavuus [31657]