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Hands Up!

Wirkestrand, Lukas LU (2024) In Master's Theses in Mathematical Sciences FMAM05 20241
Mathematics (Faculty of Engineering)
Abstract
This study delves into the potential of contactless palmprints within large-scale biometric frameworks, focusing on improving candidate narrowing through an encoder-based approach. Utilizing deep neural networks and trained via semi-hard triplet learning, the research transforms palm images into distinctive feature vectors for precise identification and candidate selection. Comprehensive analysis involving various architectures, datasets, and preprocessing techniques achieved a closed-set rank 10 retrieval rate of 99.4\% on the HandID and Tongji datasets. Additionally, the Average Number of Hands (ANH) metric was introduced for model comparison, revealing that Model 62 outperformed others across multiple tests. Although the models are not... (More)
This study delves into the potential of contactless palmprints within large-scale biometric frameworks, focusing on improving candidate narrowing through an encoder-based approach. Utilizing deep neural networks and trained via semi-hard triplet learning, the research transforms palm images into distinctive feature vectors for precise identification and candidate selection. Comprehensive analysis involving various architectures, datasets, and preprocessing techniques achieved a closed-set rank 10 retrieval rate of 99.4\% on the HandID and Tongji datasets. Additionally, the Average Number of Hands (ANH) metric was introduced for model comparison, revealing that Model 62 outperformed others across multiple tests. Although the models are not yet sufficient as standalone end-to-end classifiers, they exhibit strong potential when combined with additional classifiers. Comparisons to previous studies underscore the promising performance of palmprint biometrics, highlighting their potential in specific domains like access security and payment. (Less)
Please use this url to cite or link to this publication:
author
Wirkestrand, Lukas LU
supervisor
organization
alternative title
Training Deep Neural Network Embeddings for Contactless Palmprint Recognition
course
FMAM05 20241
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Contactless Palmprint Recognition, Machine Learning, Deep Neural Network, Triplet Learning
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMA-3542-2024
ISSN
1404-6342
other publication id
2024:E38
language
English
id
9159106
date added to LUP
2024-07-01 08:59:02
date last changed
2024-07-01 08:59:02
@misc{9159106,
  abstract     = {{This study delves into the potential of contactless palmprints within large-scale biometric frameworks, focusing on improving candidate narrowing through an encoder-based approach. Utilizing deep neural networks and trained via semi-hard triplet learning, the research transforms palm images into distinctive feature vectors for precise identification and candidate selection. Comprehensive analysis involving various architectures, datasets, and preprocessing techniques achieved a closed-set rank 10 retrieval rate of 99.4\% on the HandID and Tongji datasets. Additionally, the Average Number of Hands (ANH) metric was introduced for model comparison, revealing that Model 62 outperformed others across multiple tests. Although the models are not yet sufficient as standalone end-to-end classifiers, they exhibit strong potential when combined with additional classifiers. Comparisons to previous studies underscore the promising performance of palmprint biometrics, highlighting their potential in specific domains like access security and payment.}},
  author       = {{Wirkestrand, Lukas}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{Hands Up!}},
  year         = {{2024}},
}