ENHANCED IRIS RECOGNITION BASED ON IMAGE MATCH AND HAMMING DISTANCE

Publications

Share / Export Citation / Email / Print / Text size:

International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering , Engineering, Electrical & Electronic

GET ALERTS

eISSN: 1178-5608

DESCRIPTION

5
Reader(s)
5
Visit(s)
0
Comment(s)
0
Share(s)

VOLUME 8 , ISSUE 2 (June 2015) > List of articles

ENHANCED IRIS RECOGNITION BASED ON IMAGE MATCH AND HAMMING DISTANCE

Gao Xiaoxing / Feng Sumin / Cui Han

Keywords : Iris recognition, Score fusion, Iris location, Image match, Code matrix

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 8, Issue 2, Pages 1,085-1,103, DOI: https://doi.org/10.21307/ijssis-2017-797

License : (CC BY-NC-ND 4.0)

Received Date : 25-January-2015 / Accepted: 10-April-2015 / Published Online: 01-June-2015

ARTICLE

ABSTRACT

Iris recognition and favor because of its high recognition rate, noninvasive and simple algorithm and other advantages, in a variety of biometric identification technology is very prominent. The iris texture feature extraction is the core of the iris recognition algorithm. Fractal geometry theory provides new ideas and methods to express nonlinear image information, the fractal dimension is an important parameter of fractal geometry, is a measure of complexity of irregular change, covering blanket dimension can better reflect the graphics changes in different resolution characteristics; missing is the fractal dimension and independent statistics, is a supplement to the fractal dimension,
overcome the different texture characteristics may have the same fractal dimension of the problem. This paper presents a blanket and missing items based on the combination of texture feature extraction algorithm, can make full use of radiation in different resolution iris texture information and texture, classification ability make feature matrix has a better. Iris matching is the key of iris recognition. How to effectively carry out the matching of the iris code matrix is the decisive step in iris recognition. In this paper based on the normalized correlation classifier, the matching method of cyclic shift, eliminated from the same eyes of different iris image due to differences in rotation caused, improve matching accuracy.

Content not available PDF Share

FIGURES & TABLES

REFERENCES

[1] Stankovic, Z. ; Doncov, N. ; Russer, J. ; Asenov, T. ; Milovanovic, B,Efficient DOA
estimation of impinging stochastic EM signal using neural networks, 2013 International
Conference on Electromagnetics in Advanced Applications (ICEAA), pp.575 - 578,2013..
[2] Steve Lu, Rafail Ostrovsky, Amit Sahai, Hovav Shacham, Brent Waters. Sequential aggregate
signatures and multisignatures without random oracles. EUROCRYPT'06 Proceedings of the
24th annual international conference on The Theory and Applications of Cryptographic
Techniques, Springer, Berlin, 2006, pp.465–485.
[3] Jacques Stern, David Pointcheval, John Malone-Lee,Nigel P. Smart. Flaws in Applying Proof
Methodologies to Signature Schemes . In CRYPTO 2002, LNCS 2442,Springer, Berlin,
2002 ,pp. 93–110.
[4] Thepade, Sudeep ; Mandal, Pushpa R,Energy compaction based novel Iris recognition
techniques using partial energies of transformed iris images with Cosine, Walsh, Haar, Kekre,
Hartley Transforms and their Wavelet Transforms,2014 Annual IEEE India Conference
(INDICON), pp.1-6,2014.
[5] J.X.Wu, T.Wang, Z.Y.Suo, et al, “DOA estimation for ULA by spectral Capon rooting
method”, Electronics Letters, vol.45, No.1, 2009, pp.84-85.
[6] J.M.Xin and S.A, “Linear prediction approach to direction estimation of cyclostationary
signals in multipath environment”, IEEE Transactions on Signal Processing, vol.49, No.4,
2001, pp.710-720.
[7] E.Grosicki, K. Abed-Meraim and K.Y.Hua, “A weighted linear prediction method for nearfield
source localization “, IEEE Transactions on Signal Processing, vol.53, No.10, 2005,
pp.3651-3660.
[8] T.B.Lavate, V.K.Kokate and A.M.Sapkal, “Performance analysis of MUSIC and ESPRIT
DOA estimation algorithms for adaptive array smart antenna in mobile communication”, I
2010 Second International Conference on Computer and Network Technology (ICCNT),
2010, pp. 308 - 311.
[9] Fortunati, S. ; Grasso, R. ; Gini, F. ; Greco, M.S. Single snapshot DOA estimation using
compressed sensing, 2014 IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP), pp.2297 - 2301,2014..
[10] Chen Ningyu,A New Attack Method on Digital Signature Scheme, Computational and
Information Sciences (ICCIS), 2013 Fifth International Conference on, pp.183 - 186, 2013.
[11] Fisher, P.S. ; Min Gyung Kwak ; Eunjung Lee ; Jinsuk Baek, Signature Scheme for
Digital Imagery, 2014 International Conference on Information Science and Applications
(ICISA), pp.1-4, 2014.
[12] S. Yamada, K. Chomsuwan, S.C.Mukhopadhyay, M.Iwahara, M. Kakikawa and I.
Nagano, “Detection of Magnetic Fluid Volume Density with a GMR Sensor”, Journal of
Magnetics Society of Japan, Vol. 31, No. 2, pp. 44-47, 2007.
[13] Dai, Yue ; Su, Shenghui,A Diploma Anti-forgery System Based on Lightweight Digital
Signatures, 2014 Tenth International Conference on Computational Intelligence and Security
(CIS), pp. 647 - 651, 2014..
[14] Anna Lysyanskaya, Silvio Micali, Leonid Reyzin,Hovav Shacham. Sequential Aggregate
Signatures from Trapdoor Permutations. In EUROCRYPT 2004, LNCS 3027, Springer,
Berlin, 2004, pp. 74–90. 25
[15] O’Gorman. Comparing Passwords, Tokens, and Biometrics for User Authentication.
Proceedings of the IEEE. 2003, VOL 91:2019~2020.
[16] S.C.Mukhopadhyay, K. Chomsuwan, C. Gooneratne and S. Yamada, “A Novel Needle-
Type SV-GMR Sensor for Biomedical Applications”, IEEE Sensors Journal, Vol. 7, No. 3, pp.
401-408, March 2007.
[17] Els J. Kindt, An Introduction into the Use of Biometric Technology, Privacy and Data
Protection Issues of Biometric Applications, Law, Governance and Technology Series, vol.12,
pp.15-85, 2013.
[18] Katy Castillo-Rosado, José Hernández-Palancar,Rolled-Plain Fingerprint Images
Classification,Lecture Notes in Computer Science Volume vol. 8827, 2014, pp 556-563.
[19] Blanz V, Vetter T. Face Recognition Based on Fitting a 3D Morphable Model. IEEE
Trans on Pattern Analysis and Machine Intelligence. 2003,25(9):1063-1074.
[20] Plamondon R, Srihari S N. Online and off-line handwriting recognition: A comprehensive
survey. IEEE Trans on Pattern Analysis and Machine Intelligence. 2000, 11(1): 68-89.
[21] S.C.Mukhopadhyay, F.P.Dawson, M.Iwahara and S.Yamada, “A Novel Compact
Magnetic Current Limiter for Three Phase Applications”, IEEE Transactions on Magnetics,
Vol. 36, No. 5, pp. 3568-3570, September 2000.
[22] Little J, Boyd J. Recognizing people by their gait :The shape of motion. Journal of
Computer Vision Research. 1998,1(2):2-32.
[23] Shaoping Zhu and Yongliang Xiao, Intelligent Detection of Facial Expression based on
Image, International Journal on Smart Sensing and Intelligent Systems, 8(1):581 – 601, 2015.
[24] Nicola Ivan Giannoccaro, Luigi Spedicato, Aime, Lay-Ekuakille, A robotic arm to sort
different types of ball bearings from the knowledge discovered by size measurements of
image regions and rfid support, International Journal on Smart Sensing and Intelligent
Systems, 7(2):674 – 700, 2014.

EXTRA FILES

COMMENTS