Omar, R.R. and Han, Tingting and Al-Sumaidaee, S.A.M. and Chen, Taolue (2018) Deep finger texture learning for verifying people. IET Biometrics 8 (1), pp. 40-48. ISSN 2047-4938.
|
Text
22928a.pdf - Author's Accepted Manuscript Download (1MB) | Preview |
Abstract
Finger Texture (FT) is currently attracting significant attentions in the area of human recognition. Finger texture covers the area between the lower knuckle of the finger and the upper phalanx before the fingerprint. It involves rich features which can be efficiently used as a biometric characteristic. In this paper, we contribute to this growing area by proposing a new verification approach, i.e., Deep Finger Texture Learning (DFTL). To the best of our knowledge, this is the first time that deep learning is employed for recognizing people by using the FT characteristic. Four databases have been used to evaluate the proposed method: the Hong Kong Polytechnic University Contact-free 3D/2D (PolyU2D), Indian Institute of Technology Delhi (IITD), CASIA Blue spectral (CASIA-BLU) corresponding to spectral 460nm and CASIA White spectral (CASIA-WHT) from the CASIA Multi-Spectral images database. The obtained results have shown superior performance compared with recent literature. The Verification Accuracies (VAs) have attained 100%, 98.65%, 100% and 98% for the four databases of PolyU2D, IITD, CASIA-BLU and CASIA-WHT, respectively.
Metadata
| Item Type: | Article |
|---|---|
| Additional Information: | This paper is a postprint of a paper submitted to and accepted for publication in IET Control Theory & Applications and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library. |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Research Centres and Institutes: | Data Analytics, Birkbeck Institute for |
| Depositing User: | Tingting Han |
| Date Deposited: | 03 Jul 2018 11:42 |
| Last Modified: | 09 Aug 2023 12:44 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/22928 |
Statistics
Additional statistics are available via IRStats2.
Archive Staff Only (login required)
