- Title
- Deep palmprint recognition with alignment and augmentation of limited training samples
- Creator
- Brown, Dane L
- Creator
- Bradshaw, Karen L
- Subject
- To be catalogued
- Date Issued
- 2022
- Date
- 2022
- Type
- text
- Type
- article
- Identifier
- http://hdl.handle.net/10962/464074
- Identifier
- vital:76473
- Identifier
- xlink:href="https://doi.org/10.1007/s42979-021-00859-3"
- Description
- This paper builds upon a previously proposed automatic palmprint alignment and classification system. The proposed system was geared towards palmprints acquired from either contact or contactless sensors. It was robust to finger location and fist shape changes—accurately extracting the palmprints in images without fingers. An extension to this previous work includes comparisons of traditional and deep learning models, both with hyperparameter tuning. The proposed methods are compared with related verification systems and a detailed evaluation of open-set identification. The best results were yielded by a proposed Convolutional Neural Network, based on VGG-16, and outperforming tuned VGG-16 and Xception architectures. All deep learning algorithms are provided with augmented data, included in the tuning process, enabling significant accuracy gains. Highlights include near-zero and zero EER on IITD-Palmprint verification using one training sample and leave-one-out strategy, respectively. Therefore, the proposed palmprint system is practical as it is effective on data containing many and few training examples.
- Format
- computer
- Format
- online resource
- Format
- application/pdf
- Format
- 1 online resource (17 pages)
- Format
- Publisher
- SpringerLink
- Language
- English
- Relation
- SN Computer Science
- Relation
- Brown, D. and Bradshaw, K., 2022. Deep palmprint recognition with alignment and augmentation of limited training samples. SN Computer Science, 3(1), p.11
- Relation
- SN Computer Science volume 3 number 1 p. 11 2022 2661-8907
- Rights
- Publisher
- Rights
- Use of this resource is governed by the terms and conditions of the SpringerLink Terms of Use Statement ( https://link.springer.com/termsandconditions)
- Rights
- Open Access
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- Visitors: 108
- Downloads: 1
Collections
Brown, Dane A (Associate Prof)
RU Department of Computer Science
Bradshaw, Karen Lee (Assoc Prof)
SDG 09. Industry, Innovation and Infrastructure
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | Deep palmprint recognition with alignment and augmentation of limited training samples.pdf | 2 MB | Adobe Acrobat PDF | View Details Download |