Skin-Based Identification From Multispectral Image Data Using CNNs

Takeshi Uemori, Atsushi Ito, Yusuke Moriuchi, Alexander Gatto, Jun Murayama; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 12349-12358

Abstract


User identification from hand images only is still a challenging task. In this paper, we propose a new biometric identification system based solely on a skin patch from a multispectral image. The system is utilizing a novel modified 3D CNN architecture which is taking advantage of multispectral data. We demonstrate the application of our system for the example of human identification from multispectral images of hands. To the best of our knowledge, this paper is the first to describe a pose-invariant and robust to overlapping real-time human identification system using hands. Additionally, we provide a framework to optimize the required spectral bands for the given spatial resolution limitations.

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[bibtex]
@InProceedings{Uemori_2019_CVPR,
author = {Uemori, Takeshi and Ito, Atsushi and Moriuchi, Yusuke and Gatto, Alexander and Murayama, Jun},
title = {Skin-Based Identification From Multispectral Image Data Using CNNs},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}