A method of detection and recognition for static gesture based on Faster-RCNN and dual-streams CNN

Yali Wang; Zhiguo Liu; Yajun Wang; Miaohua Liu

doi:10.1117/12.3050038

8 November 2024 A method of detection and recognition for static gesture based on Faster-RCNN and dual-streams CNN

Yali Wang, Zhiguo Liu, Yajun Wang, Miaohua Liu

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 1341631 (2024) https://doi.org/10.1117/12.3050038
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

For improving the situation of incomplete utilization of gesture features in Static gesture recognition, this paper proposes a dual-streams convolutional neural network model using the RGB/grayscale and black-and-white images to detect and recognize static gestures for adequate learning of the images’ features and improvement of recognition accuracy rate. In this model, the K-means Clustering Method is used to remove the background, the Faster-RCNN model is used to process the image to get the gesture position, and the number of training samples is expanded to enhance the generalization of the training. Finally, two kinds of images are inputted into the network training and gotten integrated before the classification, which make full use of the color texture features and the edge shape features to improve the accuracy of recognition. Verified by Thomas Moselund and Jochen Triesch’s gesture database, the recognition accuracy reached 97.91% and 97.20%.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yali Wang, Zhiguo Liu, Yajun Wang, and Miaohua Liu "A method of detection and recognition for static gesture based on Faster-RCNN and dual-streams CNN", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 1341631 (8 November 2024); https://doi.org/10.1117/12.3050038

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KEYWORDS

Gesture recognition

Image processing

Convolutional neural networks

Education and training

Databases

Convolution

Target recognition

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