| Summary: | 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.
|
|---|