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Hong, J.S., Kim, S.G., Kim, J.S. and Park, K.R. (2024) Deep Learning-Based Restoration of Multi-Degraded Finger-Vein Image by Non-Uniform Illumination and Noise. Engineering Applications of Artificial Intelligence, 133, Article ID: 108036.
https://doi.org/10.1016/j.engappai.2024.108036

has been cited by the following article:

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

  AUTHORS: David Martínez-Martínez, Yedid Erandini Niño-Membrillo, José Francisco Solís-Villarreal, Oscar Espinoza-Ortega, Lizbeth Sandoval-Juárez, Francisco Javier Núñez-García

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

  JOURNAL NAME: Engineering, Vol.16 No.10, October 28, 2024

  ABSTRACT: 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).