Abstract
In the iris authentication system, the existence of eyelids and eyelashes noises might generate negative effect on pattern analysis. This paper proposes a novel and accurate iris segmentation method which adequately considers the edges and statistical feature to detect the eyelids and eyelashes noises in the captured iris images. First, multi-scale edge detection is used to get the iris coarse locations, and rank filter is employed to smooth images for determining a more accurate searching area of eyelids. Second, morphological operations as well as line Hough transform are presented to reserve the available edge points for multi-line eyelids fitting. Specially, the adaptive average intensity of individual iris image based on region of interest (ROI) is educed to get the statistical threshold for eyelashes detection. Experimental results indicate that the proposed method can effectively remove the occlusion caused by eyelids and eyelashes, and increase the amount of information (AOI) of segmented iris and improve the iris location accuracy.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Daugman J (2004) How iris recognition works. IEEE TCSVT, pp 21–30
Wildes RP (1997) Iris recognition: an emerging biometric technology. IEEE 85(9):1347–1363
Xu G, Zhang Z, Ma Y (2006) Improving the performance of iris recognition system using eyelids and eyelashes detection and iris image enhancement. IEEE TICCI. Beijing, pp 871–876
Daugman J (2007) New methods in iris recognition. IEEE TCMCB 37(5):1167–1175
Bastys A, Kranauskas J, Kruger V (2011) Iris recognition by fusing different representations of multi-scale Taylor expansion. Comput Vis Image Und 115(6):804–816
Du Y, Belcher C, Zhou Z (2010) Scale invariant gabor descriptor-based non-cooperative iris recognition. Signal Process 37:1–10
Farouk RM (2011) Iris recognition based on elastic graph matching and Gabor wavelets. Comput Vis Image Und 115(8):1239–1244
Roy K, Bhattacharya P, Suen CY (2011) Towards non-ideal iris recognition based on level set method, genetic algorithms and adaptive asymmetrical SVMs. Eng Appl Artif Intel 24:458–475
Qi M, Lu Y, Li J, Li X, Kong J (2008) User-specific iris authentication based on feature selection. IEEE CSSE, pp 1040–1043
Nguyen K, Fookes C, Sridharan S (2010) Fusing shrinking and expanding active contour models for robust iris segmentation. ISSPA, pp 185–188
Tajbakhsh N, Araabi BN, SoltanianZadeh H (2010) Robust iris verification based on local and global variations. Signal Process, pp 1–12
Jann F, Usman I, Agha S (2013) Reliable iris localization using Hough transform, histogram-bisection, and eccentricity. Signal Process 93(1):230–241
Hansen DW, Ji Q (2010) In the eye of the beholder: a survey of models for eyes and gaze. IEEE TPAMI 32(3):478–500
He Z, Tan T, Sun Z, Qiu XC (2008) Robust eyelid, eyelash and shadow localization for iris recognition. IEEE ICIP. San Diego, pp 265–268
Tan T, He Z, Sun Z (2008) Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition. Image Vis Comput 28(2):223–230
Sankowski W, Grabowsk KI, Zubert M, Napieralska M (2010) Reliable algorithm for iris segmentation in eye image. IVC 28(2):231–237
Li P, Liu X, Xiao L, Song Q (2010) Robust and accurate iris segmentation in very noisy iris images. IVC 28(2):246–253
Pundlik S, Woodart D, Birchfield S (2010) Iris segmentation in non-ideal images using graph cuts. IVC 28(12):1671–1681
Chen Y, Adjouadi M, Han C, Wang J, Barreto A, Rishe N, Andrian J (2010) A highly accurate and computationally efficient approach for unconstrained iris segmentation. IVC 28(2):261–269
Information on http://www.cbsr.ia.ac.cn/english/IrisDatabases.asp
Cui J et al (2004) A fast and robust iris localization method based on texture segmentation. SPIE Defense Secur Symp 5404:401–408
Prati A, Mikic I, Trivedi M, Cucchiara R (2003) Detecting moving shadows: algorithms and evaluation. IEEE TPAMI 25(7):918–923
Joshi J, Papanikolopoulos N (2008) Learning of moving cast shadows for dynamic environment. IEEE ICRA, pp 987–992
Acknowledgments
This work is supported by the Young Scientific Research Foundation of Jilin Province Science and Technology Development Project (No. 201201070), the Jilin Provincial Natural Science Foundation (No. 201115003), the Fund of Jilin Provincial Science and Technology Department (No. 20111804, No. 20110364), the Science Foundation for Post-doctor of Jilin Province (No. 2011274), the Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jia, Y., Hu, W., Yu, X., Qi, M. (2013). A Novel Iris Segmentation Method Based on Multi-Line. In: Yin, Z., Pan, L., Fang, X. (eds) Proceedings of The Eighth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 2013. Advances in Intelligent Systems and Computing, vol 212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37502-6_84
Download citation
DOI: https://doi.org/10.1007/978-3-642-37502-6_84
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37501-9
Online ISBN: 978-3-642-37502-6
eBook Packages: EngineeringEngineering (R0)