Performance evaluation of low resolution lip recognition algorithm

Article type: Research Article

Authors: Zhou, Hongcheng

Affiliations: School of Electronic and Information Engineering, Jinling Institute of Technology, Nanjing, China | E-mail: zhouhcnj@163.com

Correspondence: [*] Corresponding author: School of Electronic and Information Engineering, Jinling Institute of Technology, Nanjing, China. E-mail: zhouhcnj@163.com.

Abstract: Lip print recognition technology originated in the field of forensic medicine, and convolutional neural network has made breakthrough achievements in the field of pattern recognition and machine vision. Convolutional neural network (CNN) algorithm is rarely used in lip pattern recognition. Further exploration and research on the network model suitable for lip pattern recognition. Lip print recognition algorithm based on depth convolution neural network aims to solve the problems of complex image preprocessing, difficult feature extraction and low recognition efficiency in traditional lip print recognition algorithms. It includes collecting lip print images to establish data sets, selecting different CNN models to conduct performance evaluation experiments on low resolution lip print data sets, and analyzing the experimental results with model evaluation indicators.

Keywords: Lip print recognition, convolution neural network (CNN), low resolution, assessment

DOI: 10.3233/JCM-247482

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 4-5, pp. 2561-2569, 2024