Hand vein pattern classification using wavelet moments and convolutional neural networks

R. Castro-Ortega; C. Toxqui-Quitl; A. Padilla-Vivanco; J. Solís-Villareal; Y. Romero Hernández

doi:10.1117/12.3028429

1 October 2024 Hand vein pattern classification using wavelet moments and convolutional neural networks

R. Castro-Ortega, C. Toxqui-Quitl, A. Padilla-Vivanco, J. Solís-Villareal, Y. Romero Hernández

Proceedings Volume 13131, Current Developments in Lens Design and Optical Engineering XXV; 1313108 (2024) https://doi.org/10.1117/12.3028429
Event: Optical Engineering + Applications, 2024, San Diego, California, United States

Abstract

Vein pattern recognition is a biometric identification technology that relies on the unique vascular patterns of each person’s hand. In this paper, we propose a method for people recognition that uses a combination of wavelet invariant moments and Convolutional Neural Networks (CNNs). Wavelet moments are a set of features extracted from an image using wavelet transform. These features can be invariant to translation, rotation, and scaling, which makes them well-suited for people recognition. CNNs have been shown to be very effective for image classification tasks. In this paper, the method is evaluated on 6000 palm vein images from the PolyU Multispectral Palmprint Database. The results show that the proposed method achieves an accuracy of 99%, which is higher than the accuracy of existing methods.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

R. Castro-Ortega, C. Toxqui-Quitl, A. Padilla-Vivanco, J. Solís-Villareal, and Y. Romero Hernández "Hand vein pattern classification using wavelet moments and convolutional neural networks", Proc. SPIE 13131, Current Developments in Lens Design and Optical Engineering XXV, 1313108 (1 October 2024); https://doi.org/10.1117/12.3028429

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available