Static gesture recognition using CNN with hand landmark detection
Leyu Lyu, Dixuan Wang, Sibo Zhang, Yanran Zhao, Sicheng Zhou · 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022) · 2022
Static Gesture Recognition is an interactive system that enables people, especially the hearing-impaired, to directly communicate with other people and machines without third-party auxiliary equipment. However, it is challenging due to gesture datasets and the recognition accuracy of pre-processing. This paper proposes Convolutional Neural Network (CNN) with hand landmark detection to recognize Static Gestures and improve the pre-processing accuracy significantly. First, this paper makes a dataset by taking 5,800 photos from 4 people, including 13 one-handed gestures and 3 two-handed. Then, it pre-processes the dataset in both ways of skin color detection and hand landmark detection to select a high-accuracy method. Finally, CNN is trained for both pre-processed datasets to classify the samples into 16 classes. To verify the method’s effectiveness, this paper also compares a baseline model that comprises the Support Vector Machine and the Principle Component Analysis. The experimental result shows that CNN eclipses the other model with higher accuracy of 99% for Static Gesture Recognition.