Abstract

Finger vein verification is a promising biometric pattern for personal identification in terms of security and convenience. The recognition performance of this technology heavily relies on the quality of finger vein images and on the recognition algorithm. To achieve efficient recognition performance, a special finger vein imaging device is developed, and a finger vein recognition method based on sparse representation is proposed. The motivation for the proposed method is that finger vein images exhibit a sparse property. In the proposed system, the regions of interest (ROIs) in the finger vein images are segmented and enhanced. Sparse representation and sparsity preserving projection on ROIs are performed to obtain the features. Finally, the features are measured for recognition. An equal error rate of 0.017% was achieved based on the finger vein image database, which contains images that were captured by using the near-IR imaging device that was developed in this study. The experimental results demonstrate that the proposed method is faster and more robust than previous methods.

© 2012 Optical Society of America

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References

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  1. A. K. Jain, S. Pankanti, S. Prabhakar, H. Lin, and A. Ross, “Biometrics: a grand challenge,” in Proceedings of the 17th International Conference on Pattern Recognition (ICPR) Vol. 2, (IEEE, 2004), pp. 935–942.
  2. Z. Liu, Y. Yin, H. Wang, S. L. Song, and Q. L. Li, “Finger vein recognition with manifold learning,” J. Network Comput. Appl. 33, 275–282 (2010).
    [CrossRef]
  3. J. Hashimoto, “Finger vein authentication technology and its future,” in 2006 Symposium on VLSI Circuits Digest of Technical Papers (IEEE, 2006), pp. 5–8.
  4. H. Qin, L. Qin, and C. Yu, “Region growth-based feature extraction method for finger-vein recognition,” Opt. Eng. 50, 057208 (2011).
    [CrossRef]
  5. N. Miura, A. Nagasaka, and T. Miyatake, “Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification,” Machine Vis. Appl. 15, 194–203 (2004).
    [CrossRef]
  6. B. Huang, Y. Dai, R. Li, D. Tang, and W. Li, “Finger-vein authentication based on wide line detector and pattern normalization,” in Proceedings of the 2010 20th International Conference on Pattern Recognition (ICPR) (IEEE, 2010), pp. 1269–1272.
  7. J. Wu and C. Liu, “Finger-vein pattern identification using SVM and neural network technique,” Expert Syst. Appl. 38, 14284–14289 (2011).
    [CrossRef]
  8. W. Song, T. Kim, H. C. Kim, J. H. Choi, H. Kong, and S. Lee, “A finger-vein verification system using mean curvature,” Pattern Recogn. Lett. 32, 1541–1547 (2011).
    [CrossRef]
  9. J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009).
    [CrossRef]
  10. X. Sun, C. Lin, M. Li, H. Lin, and Q. Chen, “A DSP-based finger vein authentication system,” in Proceedings of the 2011 International Conference on Intelligent Computation Technology and Automation (ICICTA) (IEEE, 2011), pp. 333–336.
  11. Y. Liang, X. Meng, and S. An, “Canny edge detection method and its application,” Appl. Mech. Mater. 50, 483–487(2011).
    [CrossRef]
  12. M. P. Vishal, M. N. Nasser, and C. Rama, “Sparsity-motivated automatic target recognition,” Appl. Opt. 50, 1425–1433 (2011).
    [CrossRef]
  13. S. Chen and D. Donoho, “Basis pursuit,” Technical Report (Department of Statistics, Stanford University, 1994).
  14. S. Chen, D. Donoho, and M. Saunders, “Atomic decomposition by basis pursuit,” SIAM J. Sci. Comput. 20, 33–61 (1998).
    [CrossRef]
  15. J. A. Tropp and A. C. Gilbert, “Signal recovery from random measurements via orthogonal matching pursuit,” IEEE Trans. Inf. Theory 53, 4655–4666 (2007).
    [CrossRef]
  16. S. Mallat and Z. Zhang, “Matching pursuits with time-frequency dictionaries,” IEEE Trans. Signal Process. 41, 3397–3415 (1993).
    [CrossRef]
  17. L. Qiao, S. Chen, and X. Tan, “Sparsity preserving projections with applications to face recognition,” Pattern Recogn. 43, 331–341 (2010).
    [CrossRef]
  18. X. F. He, S. Yan, Y. Hu, P. Niyogi, and H. Zhang, “Face Recognition Using Laplacian faces,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 328–340 (2005).
    [CrossRef]
  19. X. F. He, C. Deng, S. Yan, and H. Zhang, “Neighborhood preserving embedding,” in Proceedings of the Tenth IEEE International Conference on Computer Vision, 2005, Vol. 2 (IEEE, 2005), pp. 1208–1213.

2011 (5)

H. Qin, L. Qin, and C. Yu, “Region growth-based feature extraction method for finger-vein recognition,” Opt. Eng. 50, 057208 (2011).
[CrossRef]

J. Wu and C. Liu, “Finger-vein pattern identification using SVM and neural network technique,” Expert Syst. Appl. 38, 14284–14289 (2011).
[CrossRef]

W. Song, T. Kim, H. C. Kim, J. H. Choi, H. Kong, and S. Lee, “A finger-vein verification system using mean curvature,” Pattern Recogn. Lett. 32, 1541–1547 (2011).
[CrossRef]

Y. Liang, X. Meng, and S. An, “Canny edge detection method and its application,” Appl. Mech. Mater. 50, 483–487(2011).
[CrossRef]

M. P. Vishal, M. N. Nasser, and C. Rama, “Sparsity-motivated automatic target recognition,” Appl. Opt. 50, 1425–1433 (2011).
[CrossRef]

2010 (2)

Z. Liu, Y. Yin, H. Wang, S. L. Song, and Q. L. Li, “Finger vein recognition with manifold learning,” J. Network Comput. Appl. 33, 275–282 (2010).
[CrossRef]

L. Qiao, S. Chen, and X. Tan, “Sparsity preserving projections with applications to face recognition,” Pattern Recogn. 43, 331–341 (2010).
[CrossRef]

2009 (1)

J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009).
[CrossRef]

2007 (1)

J. A. Tropp and A. C. Gilbert, “Signal recovery from random measurements via orthogonal matching pursuit,” IEEE Trans. Inf. Theory 53, 4655–4666 (2007).
[CrossRef]

2005 (1)

X. F. He, S. Yan, Y. Hu, P. Niyogi, and H. Zhang, “Face Recognition Using Laplacian faces,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 328–340 (2005).
[CrossRef]

2004 (1)

N. Miura, A. Nagasaka, and T. Miyatake, “Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification,” Machine Vis. Appl. 15, 194–203 (2004).
[CrossRef]

1998 (1)

S. Chen, D. Donoho, and M. Saunders, “Atomic decomposition by basis pursuit,” SIAM J. Sci. Comput. 20, 33–61 (1998).
[CrossRef]

1993 (1)

S. Mallat and Z. Zhang, “Matching pursuits with time-frequency dictionaries,” IEEE Trans. Signal Process. 41, 3397–3415 (1993).
[CrossRef]

An, S.

Y. Liang, X. Meng, and S. An, “Canny edge detection method and its application,” Appl. Mech. Mater. 50, 483–487(2011).
[CrossRef]

Chen, Q.

X. Sun, C. Lin, M. Li, H. Lin, and Q. Chen, “A DSP-based finger vein authentication system,” in Proceedings of the 2011 International Conference on Intelligent Computation Technology and Automation (ICICTA) (IEEE, 2011), pp. 333–336.

Chen, S.

L. Qiao, S. Chen, and X. Tan, “Sparsity preserving projections with applications to face recognition,” Pattern Recogn. 43, 331–341 (2010).
[CrossRef]

S. Chen, D. Donoho, and M. Saunders, “Atomic decomposition by basis pursuit,” SIAM J. Sci. Comput. 20, 33–61 (1998).
[CrossRef]

S. Chen and D. Donoho, “Basis pursuit,” Technical Report (Department of Statistics, Stanford University, 1994).

Choi, J. H.

W. Song, T. Kim, H. C. Kim, J. H. Choi, H. Kong, and S. Lee, “A finger-vein verification system using mean curvature,” Pattern Recogn. Lett. 32, 1541–1547 (2011).
[CrossRef]

Dai, Y.

B. Huang, Y. Dai, R. Li, D. Tang, and W. Li, “Finger-vein authentication based on wide line detector and pattern normalization,” in Proceedings of the 2010 20th International Conference on Pattern Recognition (ICPR) (IEEE, 2010), pp. 1269–1272.

Deng, C.

X. F. He, C. Deng, S. Yan, and H. Zhang, “Neighborhood preserving embedding,” in Proceedings of the Tenth IEEE International Conference on Computer Vision, 2005, Vol. 2 (IEEE, 2005), pp. 1208–1213.

Donoho, D.

S. Chen, D. Donoho, and M. Saunders, “Atomic decomposition by basis pursuit,” SIAM J. Sci. Comput. 20, 33–61 (1998).
[CrossRef]

S. Chen and D. Donoho, “Basis pursuit,” Technical Report (Department of Statistics, Stanford University, 1994).

Ganesh, A.

J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009).
[CrossRef]

Gilbert, A. C.

J. A. Tropp and A. C. Gilbert, “Signal recovery from random measurements via orthogonal matching pursuit,” IEEE Trans. Inf. Theory 53, 4655–4666 (2007).
[CrossRef]

Hashimoto, J.

J. Hashimoto, “Finger vein authentication technology and its future,” in 2006 Symposium on VLSI Circuits Digest of Technical Papers (IEEE, 2006), pp. 5–8.

He, X. F.

X. F. He, S. Yan, Y. Hu, P. Niyogi, and H. Zhang, “Face Recognition Using Laplacian faces,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 328–340 (2005).
[CrossRef]

X. F. He, C. Deng, S. Yan, and H. Zhang, “Neighborhood preserving embedding,” in Proceedings of the Tenth IEEE International Conference on Computer Vision, 2005, Vol. 2 (IEEE, 2005), pp. 1208–1213.

Hu, Y.

X. F. He, S. Yan, Y. Hu, P. Niyogi, and H. Zhang, “Face Recognition Using Laplacian faces,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 328–340 (2005).
[CrossRef]

Huang, B.

B. Huang, Y. Dai, R. Li, D. Tang, and W. Li, “Finger-vein authentication based on wide line detector and pattern normalization,” in Proceedings of the 2010 20th International Conference on Pattern Recognition (ICPR) (IEEE, 2010), pp. 1269–1272.

Jain, A. K.

A. K. Jain, S. Pankanti, S. Prabhakar, H. Lin, and A. Ross, “Biometrics: a grand challenge,” in Proceedings of the 17th International Conference on Pattern Recognition (ICPR) Vol. 2, (IEEE, 2004), pp. 935–942.

Kim, H. C.

W. Song, T. Kim, H. C. Kim, J. H. Choi, H. Kong, and S. Lee, “A finger-vein verification system using mean curvature,” Pattern Recogn. Lett. 32, 1541–1547 (2011).
[CrossRef]

Kim, T.

W. Song, T. Kim, H. C. Kim, J. H. Choi, H. Kong, and S. Lee, “A finger-vein verification system using mean curvature,” Pattern Recogn. Lett. 32, 1541–1547 (2011).
[CrossRef]

Kong, H.

W. Song, T. Kim, H. C. Kim, J. H. Choi, H. Kong, and S. Lee, “A finger-vein verification system using mean curvature,” Pattern Recogn. Lett. 32, 1541–1547 (2011).
[CrossRef]

Lee, S.

W. Song, T. Kim, H. C. Kim, J. H. Choi, H. Kong, and S. Lee, “A finger-vein verification system using mean curvature,” Pattern Recogn. Lett. 32, 1541–1547 (2011).
[CrossRef]

Li, M.

X. Sun, C. Lin, M. Li, H. Lin, and Q. Chen, “A DSP-based finger vein authentication system,” in Proceedings of the 2011 International Conference on Intelligent Computation Technology and Automation (ICICTA) (IEEE, 2011), pp. 333–336.

Li, Q. L.

Z. Liu, Y. Yin, H. Wang, S. L. Song, and Q. L. Li, “Finger vein recognition with manifold learning,” J. Network Comput. Appl. 33, 275–282 (2010).
[CrossRef]

Li, R.

B. Huang, Y. Dai, R. Li, D. Tang, and W. Li, “Finger-vein authentication based on wide line detector and pattern normalization,” in Proceedings of the 2010 20th International Conference on Pattern Recognition (ICPR) (IEEE, 2010), pp. 1269–1272.

Li, W.

B. Huang, Y. Dai, R. Li, D. Tang, and W. Li, “Finger-vein authentication based on wide line detector and pattern normalization,” in Proceedings of the 2010 20th International Conference on Pattern Recognition (ICPR) (IEEE, 2010), pp. 1269–1272.

Liang, Y.

Y. Liang, X. Meng, and S. An, “Canny edge detection method and its application,” Appl. Mech. Mater. 50, 483–487(2011).
[CrossRef]

Lin, C.

X. Sun, C. Lin, M. Li, H. Lin, and Q. Chen, “A DSP-based finger vein authentication system,” in Proceedings of the 2011 International Conference on Intelligent Computation Technology and Automation (ICICTA) (IEEE, 2011), pp. 333–336.

Lin, H.

X. Sun, C. Lin, M. Li, H. Lin, and Q. Chen, “A DSP-based finger vein authentication system,” in Proceedings of the 2011 International Conference on Intelligent Computation Technology and Automation (ICICTA) (IEEE, 2011), pp. 333–336.

A. K. Jain, S. Pankanti, S. Prabhakar, H. Lin, and A. Ross, “Biometrics: a grand challenge,” in Proceedings of the 17th International Conference on Pattern Recognition (ICPR) Vol. 2, (IEEE, 2004), pp. 935–942.

Liu, C.

J. Wu and C. Liu, “Finger-vein pattern identification using SVM and neural network technique,” Expert Syst. Appl. 38, 14284–14289 (2011).
[CrossRef]

Liu, Z.

Z. Liu, Y. Yin, H. Wang, S. L. Song, and Q. L. Li, “Finger vein recognition with manifold learning,” J. Network Comput. Appl. 33, 275–282 (2010).
[CrossRef]

Ma, Y.

J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009).
[CrossRef]

Mallat, S.

S. Mallat and Z. Zhang, “Matching pursuits with time-frequency dictionaries,” IEEE Trans. Signal Process. 41, 3397–3415 (1993).
[CrossRef]

Meng, X.

Y. Liang, X. Meng, and S. An, “Canny edge detection method and its application,” Appl. Mech. Mater. 50, 483–487(2011).
[CrossRef]

Miura, N.

N. Miura, A. Nagasaka, and T. Miyatake, “Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification,” Machine Vis. Appl. 15, 194–203 (2004).
[CrossRef]

Miyatake, T.

N. Miura, A. Nagasaka, and T. Miyatake, “Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification,” Machine Vis. Appl. 15, 194–203 (2004).
[CrossRef]

Nagasaka, A.

N. Miura, A. Nagasaka, and T. Miyatake, “Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification,” Machine Vis. Appl. 15, 194–203 (2004).
[CrossRef]

Nasser, M. N.

Niyogi, P.

X. F. He, S. Yan, Y. Hu, P. Niyogi, and H. Zhang, “Face Recognition Using Laplacian faces,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 328–340 (2005).
[CrossRef]

Pankanti, S.

A. K. Jain, S. Pankanti, S. Prabhakar, H. Lin, and A. Ross, “Biometrics: a grand challenge,” in Proceedings of the 17th International Conference on Pattern Recognition (ICPR) Vol. 2, (IEEE, 2004), pp. 935–942.

Prabhakar, S.

A. K. Jain, S. Pankanti, S. Prabhakar, H. Lin, and A. Ross, “Biometrics: a grand challenge,” in Proceedings of the 17th International Conference on Pattern Recognition (ICPR) Vol. 2, (IEEE, 2004), pp. 935–942.

Qiao, L.

L. Qiao, S. Chen, and X. Tan, “Sparsity preserving projections with applications to face recognition,” Pattern Recogn. 43, 331–341 (2010).
[CrossRef]

Qin, H.

H. Qin, L. Qin, and C. Yu, “Region growth-based feature extraction method for finger-vein recognition,” Opt. Eng. 50, 057208 (2011).
[CrossRef]

Qin, L.

H. Qin, L. Qin, and C. Yu, “Region growth-based feature extraction method for finger-vein recognition,” Opt. Eng. 50, 057208 (2011).
[CrossRef]

Rama, C.

Ross, A.

A. K. Jain, S. Pankanti, S. Prabhakar, H. Lin, and A. Ross, “Biometrics: a grand challenge,” in Proceedings of the 17th International Conference on Pattern Recognition (ICPR) Vol. 2, (IEEE, 2004), pp. 935–942.

Sastry, S. S.

J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009).
[CrossRef]

Saunders, M.

S. Chen, D. Donoho, and M. Saunders, “Atomic decomposition by basis pursuit,” SIAM J. Sci. Comput. 20, 33–61 (1998).
[CrossRef]

Song, S. L.

Z. Liu, Y. Yin, H. Wang, S. L. Song, and Q. L. Li, “Finger vein recognition with manifold learning,” J. Network Comput. Appl. 33, 275–282 (2010).
[CrossRef]

Song, W.

W. Song, T. Kim, H. C. Kim, J. H. Choi, H. Kong, and S. Lee, “A finger-vein verification system using mean curvature,” Pattern Recogn. Lett. 32, 1541–1547 (2011).
[CrossRef]

Sun, X.

X. Sun, C. Lin, M. Li, H. Lin, and Q. Chen, “A DSP-based finger vein authentication system,” in Proceedings of the 2011 International Conference on Intelligent Computation Technology and Automation (ICICTA) (IEEE, 2011), pp. 333–336.

Tan, X.

L. Qiao, S. Chen, and X. Tan, “Sparsity preserving projections with applications to face recognition,” Pattern Recogn. 43, 331–341 (2010).
[CrossRef]

Tang, D.

B. Huang, Y. Dai, R. Li, D. Tang, and W. Li, “Finger-vein authentication based on wide line detector and pattern normalization,” in Proceedings of the 2010 20th International Conference on Pattern Recognition (ICPR) (IEEE, 2010), pp. 1269–1272.

Tropp, J. A.

J. A. Tropp and A. C. Gilbert, “Signal recovery from random measurements via orthogonal matching pursuit,” IEEE Trans. Inf. Theory 53, 4655–4666 (2007).
[CrossRef]

Vishal, M. P.

Wang, H.

Z. Liu, Y. Yin, H. Wang, S. L. Song, and Q. L. Li, “Finger vein recognition with manifold learning,” J. Network Comput. Appl. 33, 275–282 (2010).
[CrossRef]

Wright, J.

J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009).
[CrossRef]

Wu, J.

J. Wu and C. Liu, “Finger-vein pattern identification using SVM and neural network technique,” Expert Syst. Appl. 38, 14284–14289 (2011).
[CrossRef]

Yan, S.

X. F. He, S. Yan, Y. Hu, P. Niyogi, and H. Zhang, “Face Recognition Using Laplacian faces,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 328–340 (2005).
[CrossRef]

X. F. He, C. Deng, S. Yan, and H. Zhang, “Neighborhood preserving embedding,” in Proceedings of the Tenth IEEE International Conference on Computer Vision, 2005, Vol. 2 (IEEE, 2005), pp. 1208–1213.

Yang, A. Y.

J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009).
[CrossRef]

Yin, Y.

Z. Liu, Y. Yin, H. Wang, S. L. Song, and Q. L. Li, “Finger vein recognition with manifold learning,” J. Network Comput. Appl. 33, 275–282 (2010).
[CrossRef]

Yu, C.

H. Qin, L. Qin, and C. Yu, “Region growth-based feature extraction method for finger-vein recognition,” Opt. Eng. 50, 057208 (2011).
[CrossRef]

Zhang, H.

X. F. He, S. Yan, Y. Hu, P. Niyogi, and H. Zhang, “Face Recognition Using Laplacian faces,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 328–340 (2005).
[CrossRef]

X. F. He, C. Deng, S. Yan, and H. Zhang, “Neighborhood preserving embedding,” in Proceedings of the Tenth IEEE International Conference on Computer Vision, 2005, Vol. 2 (IEEE, 2005), pp. 1208–1213.

Zhang, Z.

S. Mallat and Z. Zhang, “Matching pursuits with time-frequency dictionaries,” IEEE Trans. Signal Process. 41, 3397–3415 (1993).
[CrossRef]

Appl. Mech. Mater. (1)

Y. Liang, X. Meng, and S. An, “Canny edge detection method and its application,” Appl. Mech. Mater. 50, 483–487(2011).
[CrossRef]

Appl. Opt. (1)

Expert Syst. Appl. (1)

J. Wu and C. Liu, “Finger-vein pattern identification using SVM and neural network technique,” Expert Syst. Appl. 38, 14284–14289 (2011).
[CrossRef]

IEEE Trans. Inf. Theory (1)

J. A. Tropp and A. C. Gilbert, “Signal recovery from random measurements via orthogonal matching pursuit,” IEEE Trans. Inf. Theory 53, 4655–4666 (2007).
[CrossRef]

IEEE Trans. Pattern Anal. Mach. Intell. (2)

X. F. He, S. Yan, Y. Hu, P. Niyogi, and H. Zhang, “Face Recognition Using Laplacian faces,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 328–340 (2005).
[CrossRef]

J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009).
[CrossRef]

IEEE Trans. Signal Process. (1)

S. Mallat and Z. Zhang, “Matching pursuits with time-frequency dictionaries,” IEEE Trans. Signal Process. 41, 3397–3415 (1993).
[CrossRef]

J. Network Comput. Appl. (1)

Z. Liu, Y. Yin, H. Wang, S. L. Song, and Q. L. Li, “Finger vein recognition with manifold learning,” J. Network Comput. Appl. 33, 275–282 (2010).
[CrossRef]

Machine Vis. Appl. (1)

N. Miura, A. Nagasaka, and T. Miyatake, “Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification,” Machine Vis. Appl. 15, 194–203 (2004).
[CrossRef]

Opt. Eng. (1)

H. Qin, L. Qin, and C. Yu, “Region growth-based feature extraction method for finger-vein recognition,” Opt. Eng. 50, 057208 (2011).
[CrossRef]

Pattern Recogn. (1)

L. Qiao, S. Chen, and X. Tan, “Sparsity preserving projections with applications to face recognition,” Pattern Recogn. 43, 331–341 (2010).
[CrossRef]

Pattern Recogn. Lett. (1)

W. Song, T. Kim, H. C. Kim, J. H. Choi, H. Kong, and S. Lee, “A finger-vein verification system using mean curvature,” Pattern Recogn. Lett. 32, 1541–1547 (2011).
[CrossRef]

SIAM J. Sci. Comput. (1)

S. Chen, D. Donoho, and M. Saunders, “Atomic decomposition by basis pursuit,” SIAM J. Sci. Comput. 20, 33–61 (1998).
[CrossRef]

Other (6)

B. Huang, Y. Dai, R. Li, D. Tang, and W. Li, “Finger-vein authentication based on wide line detector and pattern normalization,” in Proceedings of the 2010 20th International Conference on Pattern Recognition (ICPR) (IEEE, 2010), pp. 1269–1272.

A. K. Jain, S. Pankanti, S. Prabhakar, H. Lin, and A. Ross, “Biometrics: a grand challenge,” in Proceedings of the 17th International Conference on Pattern Recognition (ICPR) Vol. 2, (IEEE, 2004), pp. 935–942.

J. Hashimoto, “Finger vein authentication technology and its future,” in 2006 Symposium on VLSI Circuits Digest of Technical Papers (IEEE, 2006), pp. 5–8.

X. F. He, C. Deng, S. Yan, and H. Zhang, “Neighborhood preserving embedding,” in Proceedings of the Tenth IEEE International Conference on Computer Vision, 2005, Vol. 2 (IEEE, 2005), pp. 1208–1213.

X. Sun, C. Lin, M. Li, H. Lin, and Q. Chen, “A DSP-based finger vein authentication system,” in Proceedings of the 2011 International Conference on Intelligent Computation Technology and Automation (ICICTA) (IEEE, 2011), pp. 333–336.

S. Chen and D. Donoho, “Basis pursuit,” Technical Report (Department of Statistics, Stanford University, 1994).

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Figures (6)

Fig. 1.
Fig. 1.

Finger vein verification workflow.

Fig. 2.
Fig. 2.

Illustration of the developed imaging device.

Fig. 3.
Fig. 3.

Horizontal projection of raw image [2].

Fig. 4.
Fig. 4.

Results of image enhancement using the proposed method. The upper row shows the original captured images, while the lower row shows the processed images (the intensity value of the finger vein part and its background are inverted to make them visible).

Fig. 5.
Fig. 5.

Sample finger vein images from different subjects in the database.

Fig. 6.
Fig. 6.

FAR and FRR curves of the proposed method (top) and the ROC curve (bottom).

Tables (1)

Tables Icon

Table 1. EER and Response Times

Equations (16)

Equations on this page are rendered with MathJax. Learn more.

A=[A1,,AL]RD×(nL)=[x11,,x1n|x21,,x2n||xL1,,xLn].
y=i=1Lj=1nαijxij,
y=Aα¯,
α¯=[0,,0,αi1,,αin1,0,,0,].
a^=argmina¯α¯1subject toy=Aα¯,
a^=argminα¯α¯1subject toyAα¯2ε,
y=Aα¯+η,
S=[a^1,a^2,,a^n]T,
minwi=1nwTxiwTXa^i2=minwwT(i=1n(xiXa^i)(xiXa^i)T)w.
minwwTX(1SST+STS)XTwwTXXTw.
maxwwTXSβXTwwTXXTw.
XSβXTw¯=λXXTw¯,
y˜Wy=WAα¯+η˜,
a^=argminα¯α¯1subject toy¯WAα¯2ε˜.
rk(y)=yAxk(α^)2,
Decision={1ifrk(y)θ0ifrk(y)>θ.

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