People recognition with foot pressure based on neural network

Zhenya Zhao

doi:10.1117/12.3021330

19 February 2024 People recognition with foot pressure based on neural network

Zhenya Zhao

Proceedings Volume 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023); 130630A (2024) https://doi.org/10.1117/12.3021330
Event: Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 2023, Changchun, China

Abstract

To find the suspect in forensic cases, a unique feature of shoe prints can be used. It is effective to distinguish people with shape of shoes and gait features demonstrated by many studies. To address the limitations stemming from relying solely on a single feature, such as complex equipment requirements and susceptibility to interference, this study explores a people identification system that leverages a BP neural network while integrating both shape and plantar pressure features. With 3 different approaches of data processing compared, an accuracy of 89% with no noises and 74%with10% gaussian noise. The result demonstrated that the system is effectiveness on people identification.

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

Citation Download Citation

Zhenya Zhao "People recognition with foot pressure based on neural network", Proc. SPIE 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 130630A (19 February 2024); https://doi.org/10.1117/12.3021330

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