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Notice of retraction
Vol. 34, No. 8(3), S&M3042

Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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Sensors and Materials, Volume 31, Number 3(3) (2019)
Copyright(C) MYU K.K.
pp. 923-937
S&M1825 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2019.2164
Published: March 29, 2019

High-performance Gesture Recognition System [PDF]

Yun Wu, Shaoyong Yu, and Mei Yang

(Received October 15, 2018; Accepted January 30, 2019)

Keywords: gesture recognition, down sampling method, 2×2 average filter method, discrete wavelet transform, symmetric discrete wavelet transform

With advances in technology, motion sensing has been applied in a wide range of devices. The system used for motion sensing captures images via a camera and analyzes the acquired images, also known as human–computer interaction, wherein gesture-controlled machines are considered as the most convenient kind and are most commonly used. However, the existing gesture recognition algorithm often has a long computation time and uses a huge amount of memory, preventing the algorithm from being implemented in embedded systems. To alleviate these problems, in this paper, we propose to reduce the computation time of the system by employing the lifting-based discrete wavelet transform (DWT). The different frequency bands featured in a lifting-based discrete wavelet can swiftly distinguish the face region from the hand region, reduce the image resolution, and thus reduce memory usage. In addition to recognizing gestures in a swift and accurate manner, the gesture recognition approach proposed in this study is also compatible with embedded systems. Our experimental results suggest that the proposed approach can reduce the execution time by up to 66% while achieving a high identification rate.

Corresponding author: Mei Yang


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Cite this article
Yun Wu, Shaoyong Yu, and Mei Yang, High-performance Gesture Recognition System, Sens. Mater., Vol. 31, No. 3, 2019, p. 923-937.



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