Author(s)

David Martínez-Martínez¹, Yedid Erandini Niño-Membrillo², José Francisco Solís-Villarreal¹, Oscar Espinoza-Ortega¹, Lizbeth Sandoval-Juárez¹, Francisco Javier Núñez-García¹

¹Centro Universitario UAEM Valle de Teotihuacán, Universidad Autónoma del Estado de México, Axapusco, México.
²Centro Universitario UAEM Texcoco, Universidad Autónoma del Estado de México, Texcoco, México.

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).

Palm Vein Recognition, Cross-Correlation, Haar Wavelets, Multilayer Perceptron

Martínez-Martínez, D. , Niño-Membrillo, Y. , Solís-Villarreal, J. , Espinoza-Ortega, O. , Sandoval-Juárez, L. and Núñez-García, F. (2024) People Recognition by RGB and NIR Analysis from Digital Image Database Using Cross-Correlation and Wavelets. Engineering, 16, 353-359. doi: 10.4236/eng.2024.1610026.