Local feature based retrieval approach for iris biometrics

doi:10.1007/s11704-013-3073-7

Front. Comput. Sci.

2013, Vol. 7

Issue (5) : 767-781 https://doi.org/10.1007/s11704-013-3073-7

RESEARCH ARTICLE

Local feature based retrieval approach for iris biometrics

Hunny MEHROTRA(

), Banshidhar MAJHI

Department of Computer Science and Engineering, National Institute of Technology, Rourkela 769008, India

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Abstract

This paper proposes an efficient retrieval approach for iris using local features. The features are extracted from segmented iris image using scale invariant feature transform (SIFT). The keypoint descriptors extracted from SIFT are clustered into m groups using k-means. The idea is to perform indexing of keypoints based on descriptor property. During database indexing phase, k-d tree k-dimensional tree is constructed for each cluster center taken from N iris images. Thus for m clusters, m such k-d trees are generated denoted as t_i, where 1≤i ≤m. During the retrieval phase, the keypoint descriptors from probe iris image are clustered into m groups and ith cluster center is used to traverse corresponding t_i for searching. k nearest neighbor approach is used, which finds p neighbors from each tree (t_i) that falls within certain radius r centered on the probe point in k-dimensional space. Finally, p neighbors from m trees are combined using union operation and top S matches (S ⊆ (m× p)) corresponding to query iris image are retrieved. The proposed approach has been tested on publicly available databases and outperforms the existing approaches in terms of speed and accuracy.

Keywords Indexing SIFT k-means k-d tree k nearest neighbors iris biometrics

Corresponding Author(s): Hunny MEHROTRA

Issue Date: 01 October 2013

Cite this article:

Hunny MEHROTRA,Banshidhar MAJHI. Local feature based retrieval approach for iris biometrics[J]. Front. Comput. Sci., 2013, 7(5): 767-781.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-013-3073-7
https://academic.hep.com.cn/fcs/EN/Y2013/V7/I5/767

1	J Daugman . The importance of being random: statistical principles of iris recognition. Pattern Recognition, 2003, 36(2): 279−291 https://doi.org/10.1016/S0031-3203(02)00030-4
2	J Daugman . How iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology, 2004, 14(1): 21−30 https://doi.org/10.1109/TCSVT.2003.818350
3	S Z Li , A K Jain , eds. . Encyclopedia of Biometrics. Springer US, 2009 https://doi.org/10.1007/978-0-387-73003-5
4	. Unique Identification Authority of India.
5	Iris Recognition Immigration System (IRIS).
6	A Mhatre , S Chikkerur , V Govindaraju . Indexing biometric databases using pyramid technique. In: Proceedings of the 5th International Conference on Audio-and Video-Based Biometric Person Authentication. 2005, 841−849 https://doi.org/10.1007/11527923_88
7	L Yu , D Zhang , K Wang , W Yang . Coarse iris classification using boxcounting to estimate fractal dimensions. Pattern Recognition, 2005, 38(11): 1791−1798 https://doi.org/10.1016/j.patcog.2005.03.015
8	X Qiu , Z Sun , T Tan . Global texture analysis of iris images for ethnic classification. In: Advances in Biometrics. 2005, 411−418
9	X Qiu , Z Sun , T Tan . Coarse iris classification by learned visual dictionary. In: Proceedings of the 2007 International Conference on Advances in Biometrics. 2007, 770−779
10	A Gyaourova , A Ross . Index codes for multibiometric pattern retrieval. IEEE Transactions on Information Forensics and Security, 2012, 7(2): 518−529 https://doi.org/10.1109/TIFS.2011.2172429
11	A Mhatre , S Palla , S Chikkerur , V Govindaraju . Efficient search and retrieval in biometric databases. In: Proceedings of Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series. 2005, 265−273
12	U Jayaraman , S Prakash , P Gupta . Indexing multimodal biometric databases using Kd-tree with feature level fusion. In: Proceedings of the 4th International Conference on Information Systems Security. 2008, 221−234 https://doi.org/10.1007/978-3-540-89862-7_19
13	U Jayaraman , S Prakash , P Gupta . Use of geometric features of prince pal components for indexing a biometric database. Mathematical and Computer Modelling, 2012 (in press)
14	A Munoz-Briseno , A Gago-Alonso , J Hernandez-Palancar . . Fingerprint indexing with bad quality areas. Expert Systems with Applications, 2013, 40(5): 1839−1846 https://doi.org/10.1016/j.eswa.2012.09.018
15	H Feng , J Daugman , P Zielinski . A fast search algorithm for a large fuzzy database. IEEE Transactions on Information Forensics and Security, 2008, 3(2): 203−212 https://doi.org/10.1109/TIFS.2008.920726
16	R Mukherjee , A Ross . Indexing iris images. In: Proceedings of the 19th International Conference on Pattern Recognition. 2008, 1−4
17	N Puhan , N Sudha . A novel iris database indexing method using the iris color. In: Proceedings of the 3rd IEEE Conference on Industrial Electronics and Applications. 2008, 1886−1891
18	H Mehrotra , B Srinivas , B Majhi , P Gupta . Indexing iris biometric database using energy histogram of DCT subbands. In: Proceedings of the 2009 International Conference on Contemporary Computing. 2009, 194−204
19	R Gadde , D Adjeroh , Ross A. Indexing iris images using the Burrows-Wheeler Transform. In: Proceedings of the 2010 IEEE International Workshop on Information Forensics and Security. 2010, 1−6 https://doi.org/10.1109/WIFS.2010.5711467
20	C Rathgeb , l A Uh. Iris-biometric hash generation for biometric database indexing. In: Proceedings of the 2010 International Conference on Pattern Recognition. 2010, 2848−2851 https://doi.org/10.1109/ICPR.2010.698
21	S Dey , D Samanta . Iris data indexing method using gabor energy features. IEEE Transactions on Information Forensics and Security, 2012, 7(4): 1192−1203 https://doi.org/10.1109/TIFS.2012.2196515
22	H Mehrotra , B Majhi , P Gupta . Robust iris indexing scheme using geometric hashing of SIFT keypoints. Journal of Network and Computer Applications, 2010, 33(3): 300−313 https://doi.org/10.1016/j.jnca.2009.12.005
23	H Wolfson , I Rigoutsos . Geometric hashing: an overview. IEEE Computational Science Engineering, 1997, 4(4): 10−21 https://doi.org/10.1109/99.641604
24	D Lowe . Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004, 60(2): 91−110 https://doi.org/10.1023/B:VISI.0000029664.99615.94
25	U Jayaraman , S Prakash , P Gupta . . An efficient color and texture based iris image retrieval technique. Expert Systems with Applications, 2012, 39(5): 4915−4926 https://doi.org/10.1016/j.eswa.2011.10.025
26	H Bay , A Ess , T Tuytelaars , . Van Gool L. Speeded-up robust features (SURF). Computer Vision and Image Understanding, 2008, 110(3): 346−359 https://doi.org/10.1016/j.cviu.2007.09.014
27	K Bowyer , K Hollingsworth , P Flynn . Image understanding for iris biometrics: A survey. Computer Vision and Image Understanding, 2008, 110: 281−307 https://doi.org/10.1016/j.cviu.2007.08.005
28	H Proenca , L Alexandre . Iris recognition: An analysis of the aliasing problem in the iris normalization stage. In: Proceedings of the 2006 International Conference on Computational Intelligence and Security. 2006, 1771−1774 https://doi.org/10.1109/ICCIAS.2006.295366
29	A Panda , H Mehrotra , B Majhi . Parallel geometric hashing for robust iris indexing. Journal of Real-Time Image Processing, 2011, 1−9
30	S Bakshi , H Mehrotra , B Majhi . Real-time iris segmentation based on image morphology. In: Proceedings of the 2011 International Conference on Communication, Computing & Security. 2011, 335−338
31	J Bentley . Multidimensional binary search trees used for associative searching. Communications of the ACM, 1975, 18(9): 509−517 https://doi.org/10.1145/361002.361007
32	J H Friedman , J L Bentley , R A Finkel . An algorithm for finding best matches in logarithmic expected time. ACM Transactions on Mathematical Software, 1977, 3: 209−226 https://doi.org/10.1145/355744.355745
33	M Blum , R Floyd , V Pratt , R Rivest , R Tarjan . Time bounds for selection. Journal of Computer and System Sciences, 1973, 7(4): 448−461 https://doi.org/10.1016/S0022-0000(73)80033-9
34	BATH University Database.
35	CASIA Database.
36	A Jain , P Flynn , A A Ross . Handbook of Biometrics. Springer-Verlag New York, Inc., 2007
37	J L Wayman . Error rate equations for the general biometric system. IEEE Robotics and Automation Magazine, 1999, 6(1): 35−48 https://doi.org/10.1109/100.755813

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