LED fingerprint recognition method based on transformer self-attention mechanism

Lihui Shen; Dun Li; Erfeng Zhang; Yanyu Zhang; Chenxi Su; Xiaodong Wang; Jian Zhang

doi:10.1117/12.3023789

18 March 2024 LED fingerprint recognition method based on transformer self-attention mechanism

Lihui Shen, Dun Li, Erfeng Zhang, Yanyu Zhang, Chenxi Su, Xiaodong Wang, Jian Zhang

Proceedings Volume 13104, Advanced Fiber Laser Conference (AFL2023); 131046K (2024) https://doi.org/10.1117/12.3023789
Event: Advanced Fiber Laser Conference (AFL2023), 2023, Shenzhen, China

Abstract

In this paper, a Light Emitting Diode (LED) fingerprinting method based on the Transformer self-attention mechanism is proposed to address the challenge of trusted authentication of terminal identity in visible light communication. The method leverages the unique individual characteristics of LED emission spectra for identity recognition. To extract fingerprint features from the spectral data, a parallel network structure is proposed. The one-dimensional spectral signal is input into a Convolutional Neural Network (CNN) to extract local features, while simultaneously being fed into a Transformer branch for self-attention calculation to capture global correlation features. The encoded global features from the Transformer are fused with the local features extracted by the CNN, and a feature interaction module is used to generate the final feature vector for classification. Experiment results demonstrate that on a spectral dataset from 8 different LED lights of the same model and batch, the proposed method achieves a classification accuracy of 98.33%, providing a new approach and method for secure access in visible light communication.

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

Citation Download Citation

Lihui Shen, Dun Li, Erfeng Zhang, Yanyu Zhang, Chenxi Su, Xiaodong Wang, and Jian Zhang "LED fingerprint recognition method based on transformer self-attention mechanism", Proc. SPIE 13104, Advanced Fiber Laser Conference (AFL2023), 131046K (18 March 2024); https://doi.org/10.1117/12.3023789

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

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Light emitting diodes

Transformers

Visible light communication

Lamps

Feature extraction

Data modeling

Education and training

Show All Keywords

Keywords/Phrases

Search In:

Publication Years