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

doi:10.4108/eai.11-8-2021.170669

Authors

Ran Wang Nanjing Normal University
Yifan Cui Nanjing Normal University
Xinyu Gao Nanjing Normal University
Wei Chen Nanjing Normal University
Mingbo Hu Nanjing Normal University
Qian Li Nanjing Normal University
Jiahui Wei Nanjing Normal University
XianWei Jiang Nanjing Normal University

DOI:

https://doi.org/10.4108/eai.11-8-2021.170669

Keywords:

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

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.