แจ้งเอกสารไม่ครบถ้วน, ไม่ตรงกับชื่อเรื่อง หรือมีข้อผิดพลาดเกี่ยวกับเอกสาร ติดต่อที่นี่ ==>
หากไม่มีอีเมลผู้รับให้กรอก thailis-noc@uni.net.th
หากไม่มีอีเมลผู้รับให้กรอก thailis-noc@uni.net.th
Ajmera, Pawan K.. Palm-print identification based on deep residual networks. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2021.
Palm-print identification based on deep residual networks
Name: Poonia, Poonam
Organization :
Birla Institute of Technology and Science. Department of Electrical and Electronics Engineering
Email :
poonia.poonam3@gmail.com
Name: Ajmera, Pawan K.
Organization :
Birla Institute of Technology and Science. Department of Electrical and Electronics Engineering
Email :
ajmera.pawan@gmail.com
LCSH:
Biometric identification
LCSH:
Palmprints
Abstract:
Abstract-- Biometric recognition has been an inseparable part
of security and authorization. In the last decade, palm-print
has been widely used in security access and
authentication. However, for efficient identity management
person
and access regulation neural network based classification
algorithms are required as they provide an efficient means of
adaptive feature extraction using back-propagation, leading to
better classification r'esults. This paper the
implementation of various neural networks for an efficient
presents
palm-print classification. The model is traimed using the
ResNet-18, ResNet-50 and ResNet-101 architectures using the
PolyU and IIT-Delhi palm-print databases. The evaluation of
the performance parameters indicate that the ResNet with
SURF features provides the best results in lesser number of
epochs. The results obtained are significantly better than the
traditional methods.
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2021
Modified:
2024-06-11
Issued:
2024-05-31
บทความ/Article
application/pdf
BibliograpyCitation :
In IEEE Computer Society. 2021 Fourth International Conference on Computational Intelligence and Communication Technologies (CCICT 2021) (pp.60-63). Los Alamitos, CA : IEEE Computer Society
eng
©copyrights King Mongkut's University of Technology North Bangkok
RightsAccess:
ใช้เวลา
0.021972 วินาที
Poonia, Poonam
| Title | Contributor | Type |
|---|---|---|
| Palm-print identification based on deep residual networks
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Poonia, Poonam;Ajmera, Pawan K. | บทความ/Article |
Ajmera, Pawan K.
| Title | Contributor | Type |
|---|---|---|
| Palm-print identification based on deep residual networks
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Poonia, Poonam;Ajmera, Pawan K. | บทความ/Article |