Research Article
Lip language identification via Wavelet entropy and K-nearest neighbor algorithm
@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, ๐พ๐พ-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
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 ๐พ๐พ-nearest neighbor algorithm. Wavelet transform is a set of image descriptors, and the ๐พ๐พ-nearest neighbor algorithm has high accuracy. After a large
number of experiments, we propose a lip recognition method based on Wavelet entropy and ๐พ๐พ-nearest neighbor, which combines Wavelet entropy, ๐พ๐พ-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.
Copyright ยฉ 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.