Lip language identification via Wavelet entropy and K-nearest neighbor algorithm

Ran Wang; Yifan Cui; Xinyu Gao; Wei Chen; Mingbo Hu; Qian Li; Jiahui Wei; XianWei Jiang

Research Article

Lip language identification via Wavelet entropy and K-nearest neighbor algorithm

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@ARTICLE{10.4108/eai.11-8-2021.170669,
    author={Ran Wang and Yifan Cui and Xinyu Gao and Wei Chen and Mingbo Hu and Qian Li and Jiahui Wei and XianWei Jiang},
    title={Lip language identification via Wavelet entropy and K-nearest neighbor algorithm},
    journal={EAI Endorsed Transactions on e-Learning},
    volume={7},
    number={22},
    publisher={EAI},
    journal_a={EL},
    year={2021},
    month={8},
    keywords={Lip language identification, Wavelet entropy, 𝐾𝐾-nearest neighbor, Wavelet transform, K-fold cross validation},
    doi={10.4108/eai.11-8-2021.170669}
}

Ran Wang
Yifan Cui
Xinyu Gao
Wei Chen
Mingbo Hu
Qian Li
Jiahui Wei
XianWei Jiang
Year: 2021
Lip language identification via Wavelet entropy and K-nearest neighbor algorithm
EL
EAI
DOI: 10.4108/eai.11-8-2021.170669

Ran Wang¹, Yifan Cui¹, Xinyu Gao¹, Wei Chen¹, Mingbo Hu¹, Qian Li¹, Jiahui Wei¹, XianWei Jiang¹^,*

1: School of Mathematics and Information Science, Nanjing Normal University of Special Education, Nanjing 210038, China

*Contact email: jxw@njts.edu.cn

Abstract

INTRODUCTION: Image processing technology is widely used in lip recognition, which can automatically detect and analyse the unstable shape of human lips.

OBJECTIVES: In this paper, we propose a new algorithm using Wavelet entropy (WE) and K-nearest neighbor (KNN) improves the accuracy of lip recognition.

METHODS: At present, the two most commonly used technologies are wavelet transform and 𝐾𝐾-nearest neighbor algorithm. Wavelet transform is a set of image descriptors, and the 𝐾𝐾-nearest neighbor algorithm has high accuracy. After a large
number of experiments, we propose a lip recognition method based on Wavelet entropy and 𝐾𝐾-nearest neighbor, which combines Wavelet entropy, 𝐾𝐾-nearest neighbor and K-fold cross validation.

RESULTS: This method reduces the calculation time and improves the training speed. The best result of the experiment improves the accuracy to 80.08%.

CONCLUSION: Therefore, our algorithm is superior to other state-of-the-art approaches of lip recognition.

Keywords: Lip language identification, Wavelet entropy, 𝐾𝐾-nearest neighbor, Wavelet transform, K-fold cross validation

Received: 2021-06-29
Accepted: 2021-08-05
Published: 2021-08-11
Publisher: EAI

: http://dx.doi.org/10.4108/eai.11-8-2021.170669

Copyright © 2021 Ran Wang et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.