Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2014 Nov 10;9(11):e112429.
doi: 10.1371/journal.pone.0112429. eCollection 2014.

Palm-vein classification based on principal orientation features

Yujia Zhou  1 , Yaqin Liu  2 , Qianjin Feng  1 , Feng Yang  1 , Jing Huang  1 , Yixiao Nie  2
Affiliations

Palm-vein classification based on principal orientation features

Yujia Zhou et al. PLoS One. .

Erratum in

  • PLoS One. 2014;9(12): e116446

Abstract

Personal recognition using palm-vein patterns has emerged as a promising alternative for human recognition because of its uniqueness, stability, live body identification, flexibility, and difficulty to cheat. With the expanding application of palm-vein pattern recognition, the corresponding growth of the database has resulted in a long response time. To shorten the response time of identification, this paper proposes a simple and useful classification for palm-vein identification based on principal direction features. In the registration process, the Gaussian-Radon transform is adopted to extract the orientation matrix and then compute the principal direction of a palm-vein image based on the orientation matrix. The database can be classified into six bins based on the value of the principal direction. In the identification process, the principal direction of the test sample is first extracted to ascertain the corresponding bin. One-by-one matching with the training samples is then performed in the bin. To improve recognition efficiency while maintaining better recognition accuracy, two neighborhood bins of the corresponding bin are continuously searched to identify the input palm-vein image. Evaluation experiments are conducted on three different databases, namely, PolyU, CASIA, and the database of this study. Experimental results show that the searching range of one test sample in PolyU, CASIA and our database by the proposed method for palm-vein identification can be reduced to 14.29%, 14.50%, and 14.28%, with retrieval accuracy of 96.67%, 96.00%, and 97.71%, respectively. With 10,000 training samples in the database, the execution time of the identification process by the traditional method is 18.56 s, while that by the proposed approach is 3.16 s. The experimental results confirm that the proposed approach is more efficient than the traditional method, especially for a large database.

PubMed Disclaimer

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Flowchart of the system.
Figure 2
Figure 2. Palm–vein images in the database of this study.
Figure 3
Figure 3. Palm–vein image.
Figure 4
Figure 4. 16×16 MFRAT at the directions of 0°, π/6, 2π/6, 3π/6, 4π/6, and 5π/6, in which Lk is 4 pixels wide.
Figure 5
Figure 5. 63×63 Gaussian-Radon filters at the directions of (a) 0°, (b) 30°, (c) 60°, (d) 90°, (e) 120°, and (f) 150°.
Figure 6
Figure 6. Palm–vein images at major directions of (a) 0°, (b) 30°, (c) 60°, (d) 90°, (e) 120°, and (f) 150° in PolyU, CASIA, and the database of this study.
Figure 7
Figure 7. Flowchart of the sub-classes construction.
Figure 8
Figure 8. The response time of identification process for one test sample by different coding methods at different database sizes.
See this image and copyright information in PMC

References

    1. Jain AK, Ross A, Prabhakar S (2004) An introduction to biometric recognition[J]. Circuits and Systems for Video Technology, IEEE Transactions on 14(1): 4–20.
    1. Wilson C (2011) Vein pattern recognition: a privacy-enhancing biometric[M]. CRC press.
    1. Song W, Kim T, Kim HC, Choi JH, Kong HJ, et al. (2011) A finger-vein verification system using mean curvature[J]. Pattern Recognition Letters 32(11): 1541–1547.
    1. Kumar A, Prathyusha KV (2009) Personal authentication using hand vein triangulation and knuckle shape[J]. Image Processing, IEEE Transactions on 18(9): 2127–2136. - PubMed
    1. Watanabe M, Endoh T, Shiohara M, Sasaki S (2005) Palm vein authentication technology and its applications[C]//Proceedings of the biometric consortium conference: 19–21.

Publication types

LinkOut - more resources