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People Recognition by RGB and NIR Analysis from Digital Image Database Using Cross-Correlation and Wavelets
Centro Universitario UAEM Valle de Teotihuacán, Universidad Autónoma del Estado de México, Axapusco, México
Centro Universitario UAEM Texcoco, Universidad Autónoma del Estado de México, Texcoco, México
Centro Universitario UAEM Valle de Teotihuacán, Universidad Autónoma del Estado de México, Axapusco, México
Centro Universitario UAEM Valle de Teotihuacán, Universidad Autónoma del Estado de México, Axapusco, México
Centro Universitario UAEM Valle de Teotihuacán, Universidad Autónoma del Estado de México, Axapusco, México
Centro Universitario UAEM Valle de Teotihuacán, Universidad Autónoma del Estado de México, Axapusco, México
- 1 Centro Universitario UAEM Valle de Teotihuacán, Universidad Autónoma del Estado de México, Axapusco, México
- 2 Centro Universitario UAEM Texcoco, Universidad Autónoma del Estado de México, Texcoco, México
- 3 Centro Universitario UAEM Valle de Teotihuacán, Universidad Autónoma del Estado de México, Axapusco, México
- 4 Centro Universitario UAEM Valle de Teotihuacán, Universidad Autónoma del Estado de México, Axapusco, México
- 5 Centro Universitario UAEM Valle de Teotihuacán, Universidad Autónoma del Estado de México, Axapusco, México
- 6 Centro Universitario UAEM Valle de Teotihuacán, Universidad Autónoma del Estado de México, Axapusco, México
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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).
KeywordsPalm Vein RecognitionCross-CorrelationHaar WaveletsMultilayer Perceptron
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