People Recognition by RGB and NIR Analysis from Digital Image Database Using Cross-Correlation and Wavelets  

作　　者：David Martínez-Martínez Yedid Erandini Niño-Membrillo José Francisco Solís-Villarreal Oscar Espinoza-Ortega Lizbeth Sandoval-Juárez Francisco Javier Núñez-García David Martínez-Martínez;Yedid Erandini Niño-Membrillo;José Francisco Solís-Villarreal;Oscar Espinoza-Ortega;Lizbeth Sandoval-Juárez;Francisco Javier Núñez-García(Centro Universitario UAEM Valle de Teotihuacn, Universidad Autnoma del Estado de Mxico, Axapusco, Mxico;Centro Universitario UAEM Texcoco, Universidad Autnoma del Estado de Mxico, Texcoco, Mxico)

机构地区：[1]Centro Universitario UAEM Valle de Teotihuacn, Universidad Autnoma del Estado de Mxico, Axapusco, Mxico [2]Centro Universitario UAEM Texcoco, Universidad Autnoma del Estado de Mxico, Texcoco, Mxico

出　　处：《Engineering（科研）》2024年第10期353-359,共7页工程（英文）（1947-3931）

摘　　要：This document presents a framework for recognizing people by palm vein distribution analysis using cross-correlation based signatures to obtain descriptors. Haar wavelets are useful in reducing the number of features while maintaining high recognition rates. This experiment achieved 97.5% of individuals classified correctly with two levels of Haar wavelets. This study used twelve-version of RGB and NIR (near infrared) wavelength images per individual. One hundred people were studied;therefore 4,800 instances compose the complete database. A Multilayer Perceptron (MLP) was trained to improve the recognition rate in a k-fold cross-validation test with k = 10. Classification results using MLP neural network were obtained using Weka (open source machine learning software).This document presents a framework for recognizing people by palm vein distribution analysis using cross-correlation based signatures to obtain descriptors. Haar wavelets are useful in reducing the number of features while maintaining high recognition rates. This experiment achieved 97.5% of individuals classified correctly with two levels of Haar wavelets. This study used twelve-version of RGB and NIR (near infrared) wavelength images per individual. One hundred people were studied;therefore 4,800 instances compose the complete database. A Multilayer Perceptron (MLP) was trained to improve the recognition rate in a k-fold cross-validation test with k = 10. Classification results using MLP neural network were obtained using Weka (open source machine learning software).

关 键 词：Palm Vein Recognition CROSS-CORRELATION Haar Wavelets Multilayer Perceptron 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]