Static gesture recognition using CNN with hand landmark detection

Leyu Lyu; Dixuan Wang; Sibo Zhang; Yanran Zhao; Sicheng Zhou

doi:10.1117/12.2641815

10 November 2022 Static gesture recognition using CNN with hand landmark detection

Leyu Lyu, Dixuan Wang, Sibo Zhang, Yanran Zhao, Sicheng Zhou

Proceedings Volume 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022); 123480I (2022) https://doi.org/10.1117/12.2641815
Event: 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 2022, Zhuhai, China

Abstract

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.

Citation Download Citation

Leyu Lyu, Dixuan Wang, Sibo Zhang, Yanran Zhao, and Sicheng Zhou "Static gesture recognition using CNN with hand landmark detection", Proc. SPIE 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 123480I (10 November 2022); https://doi.org/10.1117/12.2641815

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