Abstract
Feature extraction is one of the fundamental steps of any biometric recognition system. The biometric iris recognition is not an exception. In the last 30 years a lot of algorithms have been proposed seeking a better description of the texture image of the human iris. The problem still remains into find features that are robust to the different conditions in which the iris images are captured. This paper proposes a new iris texture description based on ordinal co-occurrence matrix features for iris recognition scheme that increases the recognition accuracy. The novelty of this work is the new strategy in applying robust feature extraction method for texture description in iris recognition. The experiments with the Casia-Interval, Casia-Thousands and Ubiris-v1 databases show that our scheme increases the recognition accuracy and it is robust to different condition of image capture.
Chapter PDF
Similar content being viewed by others
References
Labati, R.D., Genovese, A., Piuri, V., Scotti, F.: Iris segmentation: state of the art and innovative methods. In: Liu, C., Mago, V.K. (eds.) Cross Disciplinary Biometric Systems. ISRL, vol. 37, pp. 151–182. Springer, Heidelberg (2012)
Chou, C.T., Shih, S.W., Chen, W.S., Cheng, V., Chen, D.Y.: Non-orthogonal view iris recognition system. IEEE Transactions on Circuits and Systems for Video Technology 20(3), 417–430 (2010)
Daugman, J.G.: High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell. 15(11), 1148–1161 (1993)
Masek, L.: Recognition of human iris patterns for biometric identification. Master thesis, University of Western Australia (2003)
He, X., Shi, P.: Extraction of complex wavelet features for iris recognition. In: 19th International Conference on Pattern Recognition, ICPR 2008, pp. 1–4. IEEE (2008)
Nasare, R.M., Hase, S.G., More, V.: Iris recognition by complex wavelet transform. International Journal of Engineering and Innovative Technology (IJEIT) 1(4), 119–123 (2012)
Sun, Z., Tan, T., Qiu, X.: Graph matching iris image blocks with local binary pattern. In: Zhang, D., Jain, A.K. (eds.) ICB 2005. LNCS, vol. 3832, pp. 366–372. Springer, Heidelberg (2005)
Li, P., Liu, X., Zhao, N.: Weighted co-occurrence phase histogram for iris recognition. Pattern Recognition Letters 33(8), 1000–1005 (2012)
Tan, T., Zhang, X., Sun, Z., Zhang, H.: Noisy iris image matching by using multiple cues. Pattern Recogn. Lett. 33(8), 970–977 (2012)
Santos, G., Hoyle, E.: A fusion approach to unconstrained iris recognition. Pattern Recogn. Lett. 33(8), 984–990 (2012)
Sun, Z., Tan, T.: Ordinal measures for iris recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(12), 2211–2226 (2009)
Zhang, M., Sun, Z., Tan, T.: Deformed iris recognition using bandpass geometric features and lowpass ordinal features. In: 2013 International Conference on Biometrics (ICB), pp. 1–6 (2013)
Rahulkar, A., Holambe, R.: Ordinal measures based on directional ordinal wavelet filters. In: Iris Image Recognition. SpringerBriefs in Electrical and Computer Engineering, pp. 69–82. Springer International Publishing (2014)
Partio, M., Cramariuc, B., Gabbouj, M.: An ordinal co-occurrence matrix framework for texture retrieval. J. Image Video Process. 2007(1), 1–1 (2007)
Proença, H., Alexandre, L.A.: UBIRIS: a noisy iris image database. In: Roli, F., Vitulano, S. (eds.) ICIAP 2005. LNCS, vol. 3617, pp. 970–977. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Chacón-Cabrera, Y., Zhang, M., Garea-Llano, E., Sun, Z. (2015). Iris Texture Description Using Ordinal Co-occurrence Matrix Features. In: Pardo, A., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2015. Lecture Notes in Computer Science(), vol 9423. Springer, Cham. https://doi.org/10.1007/978-3-319-25751-8_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-25751-8_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25750-1
Online ISBN: 978-3-319-25751-8
eBook Packages: Computer ScienceComputer Science (R0)