Dr. Shuqiang Yang Profile

Dr. Shuqiang Yang

at Luoyang Normal Univ

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 27 March 2024 Paper

Canny-based palm vein recognition algorithm

Zhipeng Xiong, Hongchao Liao, Shanshan Wang, Yiquan Wu, Huafeng Qin, Shuqiang Yang

Proceedings Volume 13105, 131052F (2024) https://doi.org/10.1117/12.3026361

KEYWORDS: Veins, Visual process modeling, Data modeling, Neural networks, Convolution, Feature extraction, Transformers, Convolutional neural networks, Image segmentation, Visualization

Read Abstract +

As vein identification technology advances, more and more vein recognition products are coming into the public's view. Additionally, vein recognition technology boasts a much higher level of security than previous recognition technologies. The convolutional neural network model is frequently used in vein recognition research to increase vein recognition accuracy. However, because the model learns the noise in the image, vein recognition accuracy cannot be further increased. To tackle the aforementioned issues, a neural network model based on Canny is suggested for the detection of palm veins. The input image's feature information was extracted using the convolutional layer, controlled by the Canny layer to learn the vein image's texture features. The self-attention layer was then utilized to increase the convolutional layer's robustness when learning the vein image's feature information. Based on the experimental results, it can be concluded that the Canny-based vein recognition neural network model performs better at extracting texture features from vein images and avoids the network from learning unnecessary feature information. It has also demonstrated improved accuracy and equal error rates on three publicly available palm vein datasets: the CASIA-PV200 palm vein dataset (94.33% and 5.83%), the TJU-PV600 palm vein dataset (94.88% and 4.86%), and the VERA-PV220 palm vein dataset (95.64% and 4.86%).

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

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:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years