Vein pattern classification using convolutional neuronal network and moment invariants

A. Teresa Vargas-Barona; M. Angélica Espejel-Rivera; Raúl Castro-Ortega; Carina Toxqui-Quitl; Alfonso Padilla-Vivanco

doi:10.1117/12.2677811

4 October 2023 Vein pattern classification using convolutional neuronal network and moment invariants

A. Teresa Vargas-Barona, M. Angélica Espejel-Rivera, Raúl Castro-Ortega, Carina Toxqui-Quitl, Alfonso Padilla-Vivanco

Proceedings Volume 12674, Applications of Digital Image Processing XLVI; 126741A (2023) https://doi.org/10.1117/12.2677811
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

Conference Poster

Abstract

Vein pattern recognition is a novel method to reliably identify or authenticate a person’s safety. It uses infrared images from the palm, wrist, or fingers, which shows the network of veins under the skin. This paper presents a Convolutional Neural Network (CNN) to classify infrared images of the hand vein pattern. The public PolyU Database is used to train the CNN. The CNN can classify 6000 vein patterns of the hand with an accuracy of 92.81%. Even more, its performance is compared with the invariant moment descriptors. In this case, vein pattern recognition is carried out on the raw images using k-Nearest Neighbors (k-NN) and invariant Zernike moments. An accuracy of 99.97% is obtained.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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A. Teresa Vargas-Barona, M. Angélica Espejel-Rivera, Raúl Castro-Ortega, Carina Toxqui-Quitl, and Alfonso Padilla-Vivanco "Vein pattern classification using convolutional neuronal network and moment invariants", Proc. SPIE 12674, Applications of Digital Image Processing XLVI, 126741A (4 October 2023); https://doi.org/10.1117/12.2677811

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