Efficient Feature Fusion for Noise Iris in Unconstrained Environment
This paper presents an efficient fusion algorithm for
iris images to generate stable feature for recognition in unconstrained
environment. Recently, iris recognition systems are focused on real
scenarios in our daily life without the subject’s cooperation. Under
large variation in the environment, the objective of this paper is to
combine information from multiple images of the same iris. The
result of image fusion is a new image which is more stable for further
iris recognition than each original noise iris image. A wavelet-based
approach for multi-resolution image fusion is applied in the fusion
process. The detection of the iris image is based on Adaboost
algorithm and then local binary pattern (LBP) histogram is then
applied to texture classification with the weighting scheme.
Experiment showed that the generated features from the proposed
fusion algorithm can improve the performance for verification system
through iris recognition.
[1] J. G. Daugman, “High confidence visual recognition of persons by a test
of statistical independence,” IEEE Trans. Pattern Anal. Machine Int.,
vol. 15, no. 11, 1993, pp. 1148–1161.
[2] J. G. Daugman, “How iris recognition works,” IEEE Trans. Circuits
Systems Video Technol., vol. 14, no. 1, 2004, pp. 21–29.
[3] P. W. Hallinan, “Recognizing human eyes,” Geometric Methods in
Computer Vision, vol. 1570, pp. 214–226, 1991.
[4] K. W. Bowyer, K. Hollingsworth, P.J. Flynn, Image understanding for
iris biometrics: a survey, Computer Vision Image Understanding, vol.
110, no. 2, 2008.
[5] H. Proença, S. Filipe, R. Santos, J. Oliveira, and L. A. Alexandre, “The
UBIRIS.v2: A database of visible wavelength iris images captured onthe-
move and at-adistance,” IEEE Trans. Pattern Anal. Machine Intell.,
vol. 32, 2010, pp. 1529–1535.
[6] J. Daugman, “New methods in iris recognition,” IEEE Trans. Systems,
Man, Cybernet., Part B vol. 37, 2007, pp. 1167–1175.
[7] Z. Sun and T. Tan, “Ordinal measures for iris recognition,” IEEE Trans.
Pattern Anal. Machine Intell., vol. 31, 2009, pp. 2211–2226.
[8] P. Li and H. Ma, “Iris recognition in non-ideal imaging conditions,”
Pattern Recognition Letters, vol. 33, no. 8, 2012, pp. 1012–1018.
[9] H. Proença, L.A. Alexandre, Iris segmentation methodology for
noncooperative recognition, IEEE Proceedings of the Vision, Image and
Signal Processing, vol. 153, no. 2, 2006, pp. 199–205.
[10] J. G. Daugman, New methods in iris recognition, IEEE Transactions on
System, Man, and Cybernetics–Part B: Cybernetics, vol. 37, no. 5, 2007,
pp. 1167–1175.
[11] K. W. Bowyer, K. Hollingsworth, and P. J. Flynn, “Image understanding
for iris biometrics: A survey,” Computer Vision and Image
Understanding, vol. 110, 2008, pp. 281–307.
[12] H. Proença, L.A. Alexandre, The NICE.I: noisy iris challenge evaluation
– part I, in: Proceedings of the of First International Conference on
Biometrics: Theory, Applications, and Systems, 2007, pp. 1–4.
[13] H. Proença, S. Filipe, R. Santos, J. Oliveira, and L. A. Alexandre, “The
UBIRIS.v2: A database of visible wavelength iris images captured onthe-
move and at-a-distance,” IEEE Trans. Pattern Anal. Machine Intell.,
vol. 32, 2010, pp. 1529–1535.
[14] Y. H. Tsai, “A Weighted Approach to Unconstrained Iris Recognition,”
in Proc. International Conference on Computer Science and Intelligent
Systems, London, UK, 2014.
[15] Iris image database. http://socia-lab.di.ubi.pt/
[16] Y. Moses, S. Edelman, and S. Ullamn, “Generalization on Novel Images
in Upright and Inverted Faces,” Perception, vol. 25, 1996, pp. 443-461.
[17] P. J. Phillips and Y. Vardi, “Efficient Illumination Normalization of
Facial Images,” Pattern Recognition Letters, vol. 17, 1996, pp. 921-927.
[18] P. Viola, M. J. Jonse, “Robust Real-Time Face Detection,” International
Journal of Computer Vision, vol. 57, no. 2, 2004, pp. 137-154.
[19] Y. Freund and R. E. Schapire, “Experiments with a New Boosting
Aalgorithm,” in Proc. 13th International Conference on Machine
Learning, Bari, Italy, 1996, pp. 148-156.
[20] R. E. Schapire and Y. Singer, “Improved Boosting Algorithms Using
Confidence-rated Predictions.” Machine Learning, vol. 37, no. 3, 1999,
pp. 297-336.
[21] J. Friedman, T. Hastie, and R. Tibshirani, “Additive Logistic Regression:
a Statistical View of Boosting,” The Annals of Statistics, vol.28, no.2,
2000, pp. 337-407.
[22] L. L. Huang, A. Shimizu, “A multi-expert approach for robust face
detection,” Pattern Recognition, vol. 39, 2006, pp. 1695-1703.
[23] T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale
and rotation invariant texture classification with local binary patterns,”
IEEE Trans. on PAMI, vol. 24, no. 7, 2002, pp. 971–987.
[24] V. Vezhnevets, V. Sazonov and A. Andreeva, “A Survey on Pixel-Based
Skin Color Detection Techniques” Proc. of Graphicon conference, 2003.
[25] G. Pajares and J. M. de la Cruz” A wavelet-based image fusion tutorial”,
Pattern Recognition Volume 37, Issue 9, September (2004), pp. 1855-
1872
[1] J. G. Daugman, “High confidence visual recognition of persons by a test
of statistical independence,” IEEE Trans. Pattern Anal. Machine Int.,
vol. 15, no. 11, 1993, pp. 1148–1161.
[2] J. G. Daugman, “How iris recognition works,” IEEE Trans. Circuits
Systems Video Technol., vol. 14, no. 1, 2004, pp. 21–29.
[3] P. W. Hallinan, “Recognizing human eyes,” Geometric Methods in
Computer Vision, vol. 1570, pp. 214–226, 1991.
[4] K. W. Bowyer, K. Hollingsworth, P.J. Flynn, Image understanding for
iris biometrics: a survey, Computer Vision Image Understanding, vol.
110, no. 2, 2008.
[5] H. Proença, S. Filipe, R. Santos, J. Oliveira, and L. A. Alexandre, “The
UBIRIS.v2: A database of visible wavelength iris images captured onthe-
move and at-adistance,” IEEE Trans. Pattern Anal. Machine Intell.,
vol. 32, 2010, pp. 1529–1535.
[6] J. Daugman, “New methods in iris recognition,” IEEE Trans. Systems,
Man, Cybernet., Part B vol. 37, 2007, pp. 1167–1175.
[7] Z. Sun and T. Tan, “Ordinal measures for iris recognition,” IEEE Trans.
Pattern Anal. Machine Intell., vol. 31, 2009, pp. 2211–2226.
[8] P. Li and H. Ma, “Iris recognition in non-ideal imaging conditions,”
Pattern Recognition Letters, vol. 33, no. 8, 2012, pp. 1012–1018.
[9] H. Proença, L.A. Alexandre, Iris segmentation methodology for
noncooperative recognition, IEEE Proceedings of the Vision, Image and
Signal Processing, vol. 153, no. 2, 2006, pp. 199–205.
[10] J. G. Daugman, New methods in iris recognition, IEEE Transactions on
System, Man, and Cybernetics–Part B: Cybernetics, vol. 37, no. 5, 2007,
pp. 1167–1175.
[11] K. W. Bowyer, K. Hollingsworth, and P. J. Flynn, “Image understanding
for iris biometrics: A survey,” Computer Vision and Image
Understanding, vol. 110, 2008, pp. 281–307.
[12] H. Proença, L.A. Alexandre, The NICE.I: noisy iris challenge evaluation
– part I, in: Proceedings of the of First International Conference on
Biometrics: Theory, Applications, and Systems, 2007, pp. 1–4.
[13] H. Proença, S. Filipe, R. Santos, J. Oliveira, and L. A. Alexandre, “The
UBIRIS.v2: A database of visible wavelength iris images captured onthe-
move and at-a-distance,” IEEE Trans. Pattern Anal. Machine Intell.,
vol. 32, 2010, pp. 1529–1535.
[14] Y. H. Tsai, “A Weighted Approach to Unconstrained Iris Recognition,”
in Proc. International Conference on Computer Science and Intelligent
Systems, London, UK, 2014.
[15] Iris image database. http://socia-lab.di.ubi.pt/
[16] Y. Moses, S. Edelman, and S. Ullamn, “Generalization on Novel Images
in Upright and Inverted Faces,” Perception, vol. 25, 1996, pp. 443-461.
[17] P. J. Phillips and Y. Vardi, “Efficient Illumination Normalization of
Facial Images,” Pattern Recognition Letters, vol. 17, 1996, pp. 921-927.
[18] P. Viola, M. J. Jonse, “Robust Real-Time Face Detection,” International
Journal of Computer Vision, vol. 57, no. 2, 2004, pp. 137-154.
[19] Y. Freund and R. E. Schapire, “Experiments with a New Boosting
Aalgorithm,” in Proc. 13th International Conference on Machine
Learning, Bari, Italy, 1996, pp. 148-156.
[20] R. E. Schapire and Y. Singer, “Improved Boosting Algorithms Using
Confidence-rated Predictions.” Machine Learning, vol. 37, no. 3, 1999,
pp. 297-336.
[21] J. Friedman, T. Hastie, and R. Tibshirani, “Additive Logistic Regression:
a Statistical View of Boosting,” The Annals of Statistics, vol.28, no.2,
2000, pp. 337-407.
[22] L. L. Huang, A. Shimizu, “A multi-expert approach for robust face
detection,” Pattern Recognition, vol. 39, 2006, pp. 1695-1703.
[23] T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale
and rotation invariant texture classification with local binary patterns,”
IEEE Trans. on PAMI, vol. 24, no. 7, 2002, pp. 971–987.
[24] V. Vezhnevets, V. Sazonov and A. Andreeva, “A Survey on Pixel-Based
Skin Color Detection Techniques” Proc. of Graphicon conference, 2003.
[25] G. Pajares and J. M. de la Cruz” A wavelet-based image fusion tutorial”,
Pattern Recognition Volume 37, Issue 9, September (2004), pp. 1855-
1872
@article{"International Journal of Information, Control and Computer Sciences:69204", author = "Yao-Hong Tsai", title = "Efficient Feature Fusion for Noise Iris in Unconstrained Environment", abstract = "This paper presents an efficient fusion algorithm for
iris images to generate stable feature for recognition in unconstrained
environment. Recently, iris recognition systems are focused on real
scenarios in our daily life without the subject’s cooperation. Under
large variation in the environment, the objective of this paper is to
combine information from multiple images of the same iris. The
result of image fusion is a new image which is more stable for further
iris recognition than each original noise iris image. A wavelet-based
approach for multi-resolution image fusion is applied in the fusion
process. The detection of the iris image is based on Adaboost
algorithm and then local binary pattern (LBP) histogram is then
applied to texture classification with the weighting scheme.
Experiment showed that the generated features from the proposed
fusion algorithm can improve the performance for verification system
through iris recognition.
", keywords = "Image fusion, iris recognition, local binary pattern,
wavelet.", volume = "9", number = "1", pages = "329-4", }
