ML Anthology

Skin-Based Identification from Multispectral Image Data Using CNNs

Takeshi Uemori, Atsushi Ito, Yusuke Moriuchi, Alexander Gatto, Jun Murayama
CVPR 2019
doi:10.1109/CVPR.2019.01263 /cvpr/2019/uemori2019cvpr-skinbased/

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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Cite

Text

Uemori et al. "Skin-Based Identification from Multispectral Image Data Using CNNs." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. doi:10.1109/CVPR.2019.01263

Markdown

[Uemori et al. "Skin-Based Identification from Multispectral Image Data Using CNNs." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.](https://mlanthology.org/cvpr/2019/uemori2019cvpr-skinbased/) doi:10.1109/CVPR.2019.01263

BibTeX

@inproceedings{uemori2019cvpr-skinbased,
  title     = {{Skin-Based Identification from Multispectral Image Data Using CNNs}},
  author    = {Uemori, Takeshi and Ito, Atsushi and Moriuchi, Yusuke and Gatto, Alexander and Murayama, Jun},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2019},
  doi       = {10.1109/CVPR.2019.01263},
  url       = {https://mlanthology.org/cvpr/2019/uemori2019cvpr-skinbased/}
}