IWBC and LFD for Static and Dynamic Hand Gesture Recognition
Raouf Sebbah, Fatma Zohra Chelali · 2024
Sign language or non-verbal language, which is based on hand gestures, is considered as a crucial communication system that facilitates interaction between the hearing-impaired people and the hearing community. The objective of the present study is to identify static and dynamic hand gestures through implementing a sign recognition system using two descriptors namely Low Frequency Descriptor (LFD) and Improved Weber Binary Coding (IWBC). The classification system is based on SVM one-against-all approach with RBF Kernel and K-nearest neighbor (KNN). The recognition system used was tested on four datasets, comprising a dynamic one which is Sebastien Marcel’s Dynamic dataset and three static ones which are Jochen Triesch’s dataset, Arabic Sign language 2018 dataset (ArSL 2018) and Arabic Sign language dataset 2001 (Halawani). The results revealed that higher recognition rates were achieved by IWBC using SVM, with 97.07% on Arabic Sign language 2018 dataset and 95% on Jochen Triesch’s dataset. Regarding LFD, a higher recognition rate of 100% was obtained on Sebastien Marcel’s Dynamic Dataset using SVM.