Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Wireless Personal Communications
Erschienen in: Wireless Personal Communications 2/2018

26.12.2017

Robust Iris Recognition Using Moment Invariants

verfasst von: Bineet Kaur, Sukhwinder Singh, Jagdish Kumar

Erschienen in: Wireless Personal Communications | Ausgabe 2/2018

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Iris recognition under less constrained environment poses a challenge to be considered for high-security applications. In this paper, discrete orthogonal moment-based features including Tchebichef, Krawtchouk and Dual-Hahn are proposed which prove to be effective for both near-infrared and visible images. The local as well as global features are extracted from localized iris regions till 15th order with invariance (scale, rotation, translation and illumination) properties and tolerance to noise. The performance of the moment-based features is evaluated on four publicly available databases: CASIA-IrisV4-Interval, IITD.v1, UPOL and UBIRIS.v2. It is found that the proposed method gives encouraging results in terms of accuracy, equal error rate and decidability index as compared to the competing techniques available in the literature.
Vorheriger Artikel Cluster-Based Opportunistic Spectrum Allocation in CRWMN’s Using Co-operative Mechanism
Nächster Artikel Dual Feed Pattern Reconfigurable Antenna for Wireless Applications

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
2.
Tan, C. W., & Kumar, A. (2013). Adaptive and localized iris weight map for accurate iris recognition under less constrained environments. In Proceedings of IEEE 6th International Conference on Biometrics, Theory Applications and Systems (BTAS) (pp. 1–7).
3.
Daugman, J. (1993). High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(1), 1148–1161.CrossRef
4.
Wildes, R. (1997). Iris recognition: An emerging biometric technology. Proceedings of IEEE, 85(9), 1348–1363.CrossRef
5.
6.
Boles, W., & Boashash, B. (1998). A human identification technique using images of the Iris and wavelet transform. IEEE Transactions on Signal Processing, 46(4), 1185–1188.CrossRef
7.
Monro, D. M., Rakshit, S., & Zhang, D. (2007). DCT based iris recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(4), 586–595.CrossRef
8.
Ma, L., Tan, T., Wang, Y., & Zhang, D. (2004). Efficient iris recognition by characterizing key local variations. IEEE Transactions on Image Processing, 13(6), 739–750.CrossRef
9.
Ma, L., Tan, T., Wang, Y., & Zhang, D. (2004). Local intensity variation analysis for iris recognition. Pattern Recognition, 37(6), 1287–1298.CrossRef
10.
Kumar, A., & Passi, A. (2010). Comparison and combination of iris matchers for reliable personal authentication. Pattern Recognition, 43(3), 1016–1026.CrossRefMATH
11.
Rai, H., & Yadav, A. (2014). Iris recognition using combined support vector machine and Hamming distance approach. Expert Systems with Applications, 41(2), 588–593.CrossRef
12.
Liu, Y., He, F., Zhu, X., Liu, Z., Chen, Y., Han, Y., et al. (2015). The improved characteristics of bionic gabor representations by combining with sift key-points for iris recognition. Journal of Bionic Engineering, 12(3), 504–517.CrossRef
13.
Bhateja, A. K., Sharma, S., Chaudhury, S., & Agrawal, N. (2016). Iris recognition based on sparse representation and k-nearest subspace with genetic algorithm. Pattern Recognition Letters, 73, 13–18.CrossRef
14.
Umer, S., Dhara, B. C., & Chanda, B. (2015). Iris recognition using multiscale morphologic features. Pattern Recognition Letters, 65, 67–74.CrossRef
15.
Raffei, A. F. M., Asmuni, H., Hassan, R., & Othman, R. M. (2013). Feature extraction for different distances of visible reflection iris using multiscale sparse representation of local Radon transform. Pattern Recognition, 46(10), 2622–2633.CrossRef
16.
Sibai, F. N., Hosani, H. I., Naqbi, R. M., Dhanhani, S., & Shehhi, S. (2011). Iris recognition using artificial neural networks. Expert Systems with Applications, 38(5), 5940–5946.CrossRef
17.
Tan, C. W., & Kumar, A. (2012). Unified framework for automated iris segmentation using distantly acquired face images. IEEE Transactions on Image Processing, 21(9), 4068–4079.MathSciNetCrossRefMATH
18.
Proenca, H. (2010). Iris recognition: On the segmentation of degraded images acquired in the visible wavelength. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(8), 1502–1516.CrossRef
19.
Sankowski, W., Grabowski, K., Napieralska, M., Zubert, M., & Napieralski, A. (2010). Reliable algorithm for iris segmentation in eye image. Image and Vision Computing, 28(2), 231–237.CrossRef
20.
Tan, C. W., & Kumar, A. (2014). Accurate iris recognition at a distance using stabilized iris encoding and Zernike moments phase features. IEEE Transactions on Image Processing, 23(9), 3962–3974.MathSciNetCrossRefMATH
21.
Alvarez-Betancourt, Y., & Garcia-Silvente, M. (2016). A keypoints-based feature extraction method for iris recognition under variable image quality conditions. Knowledge-Based Systems, 92, 169–182.CrossRef
22.
Bowyer, K. W. (2012). The results of the NICE. II iris biometrics competition. Pattern Recognition Letters, 33(8), 965–969.CrossRef
23.
Proenca, H., & Alexandre, L. A. (2012). Toward covert iris biometric recognition: Experimental results from the NICE contests. IEEE Transactions on Information Forensics and Security, 7(2), 798–808.CrossRef
24.
25.
26.
Proenca, H., Filipe, S., Santos, R., Oliveira, J., & Alexandre, L. A. (2010). The UBIRIS.v2: A database of visible wavelength iris images captured on the move and at-a-distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(8), 1529–1535.CrossRef
27.
Yap, P. T., Raveendran, P., & Ong, S. H. (2003). Image analysis by Krawtchouk moments. IEEE Transactions on Image Processing, 12(11), 1367–1377.MathSciNetCrossRef
28.
Rahman, S. M., Howlader, T., & Hatzinakos, D. (2016). On the selection of 2D Krawtchouk moments for face recognition. Pattern Recognition, 54, 83–93.CrossRef
29.
Priyal, S. P., & Bora, P. K. (2013). A robust static hand gesture recognition system using geometry based normalizations and Krawtchouk moments. Pattern Recognition Letters, 46(8), 2202–2219.CrossRefMATH
30.
Kaur, B., Joshi, G., & Vig, R. (2015). Analysis of shape recognition capability of Krawtchouk moments. In IEEE international conference on computing, communication & automation (ICCCA) (pp. 1085–1090).
31.
Kaur, B., & Joshi, G. (2016). Lower order Krawtchouk moment-based feature-set for hand gesture recognition. Advances in Human-Computer Interaction, 2016(2016), 1–10.CrossRef
32.
Kaur, B., Joshi, G., & Vig, R. (2017). Indian sign language recognition using Krawtchouk moment-based local features. The Imaging Science Journal, 65(3), 171–179.CrossRef
33.
Kaur, B., Joshi, G., & Vig, R. (2017). Identification of ISL alphabets using discrete orthogonal moments. Wireless Personal Communications, 95, 1–23.CrossRef
34.
Tsougenis, E. D., Papakostas, G. A., Koulouriotis, D. E., & Tourassis, V. D. (2012). Performance evaluation of moment-based watermarking methods: A review. Journal of Systems and Software, 85(8), 1864–1884.CrossRef
35.
Hu, M. K. (1962). Visual pattern recognition by moment invariants. IRE Transactions on Information Theory, 8(2), 179–187.CrossRefMATH
36.
Teague, M. R. (1980). Image analysis via the general theory of moments. Journal of Optic Society of America, 70(8), 920–930.MathSciNetCrossRef
37.
Mukundan, R., & Lee, P. A. (2001). Image analysis by Tchebichef moments. IEEE Transactions on Image Processing, 10(9), 1357–1364.MathSciNetCrossRefMATH
38.
Yap, P. T., Raveendran, P., & Ong, S. H. (2007). Image analysis using Hahn moments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(11), 2057–2062.CrossRef
39.
Roy, K., & Bhattacharya, P. (2007). Iris recognition based on zigzag collarette region and asymmetrical support vector machines. International Conference Image Analysis and Recognition, 32, 854–865.CrossRef
40.
Daugman, J. (2004). How iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology, 14(1), 21–30.CrossRef
41.
Erbilek, M., & Toygar, O. (2009). Recognizing partially occluded irises using subpattern based approaches. In Proceedings of the IEEE International Symposium on Computer And Information Sciences (pp. 606–610).
42.
Chen, C., & Chu, C. (2009). High performance iris recognition based on 1-D circular feature extraction and PSO–PNN classifier. Expert Systems with Applications, 36(7), 10351–10356.CrossRef
43.
Patil, C. M., & Patilkulkarani, S. (2009). An approach of iris feature extraction for personal identification. In International conference on advances in recent technologies in communication and computing (pp. 796–799).
44.
Rahulkar, A. D., & Holambe, R. S. (2012). Half iris feature extraction and recognition using a new class of biorthogonal triplet half-band filter bank and flexible k-out-of-n: A postclassifier. The IEEE Transactions on Information Forensics and Security, 7(1), 230–240.CrossRef
45.
Elgamal, M., & Al-Biqami, N. (2013). An efficient feature extraction method for iris recognition based on wavelet transformation. International Journal of Information Technology, 2(3), 521–527.
46.
Ahamed, A., & Bhuiyan, M. I. H. (2012). Low complexity iris recognition using curvelet transform. In IEEE proceedings of the international conference on informatics, electronics & vision (pp. 548–553).
47.
Demirel, H., & Anbarjafari, G. (2008). Iris recognition system using combined histogram statistics. In Proceedings of the IEEE international symposium on computer and information sciences (pp. 1–4).
48.
Ross, A., & Sunder, M. S. (2010). Block based texture analysis for iris classification and matching. In IEEE proceedings of the workshops on computer vision and pattern recognition (pp. 30–37).
49.
Harjoko, A., Hartati, S., & Dwiyasa, H. (2009). A method for iris recognition based on 1d coiflet wavelet. World Academy of Science, Engineering and Technology, 56(24), 126–129.
50.
Masood, K., Javed, D., & Basit, A. (2007). Iris recognition using wavelet. In IEEE proceedings of the international conference on emerging technologies (pp. 253–256).
51.
Tsai, C. C., Lin, H. Y., Taur, J., & Tao, C. W. (2012). Iris recognition using possibilistic fuzzy matching on local features. IEEE Transactions on Systems, Man, and Cybernetics, 42(1), 150–162.CrossRef
52.
Pillai, J. K., Patel, V. M., Chellappa, R., & Ratha, N. K. (2011). Secure and robust iris recognition using random projections and sparse representations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(9), 1877–1893.CrossRef
53.
Wenbo, D., Zhenan, S., & Tieniu, T. (2011). Iris matching based on personalized weight map. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(9), 1744–1757.CrossRef
54.
Hollingsworth, K. P., Bowyer, K. W., & Flynn, P. J. (2009). The best bits in an iris code. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(6), 964–973.CrossRef
55.
Miyazawa, K., Ito, K., Aoki, T., Kobayashi, K., & Nakajima, H. (2008). An effective approach for iris recognition using phase-based image matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(10), 1741–1756.CrossRef
Metadaten
Titel
Robust Iris Recognition Using Moment Invariants
verfasst von
Bineet Kaur
Sukhwinder Singh
Jagdish Kumar
Publikationsdatum
26.12.2017
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 2/2018
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-017-5153-8

Weitere Artikel der Ausgabe 2/2018

Wireless Personal Communications 2/2018 Zur Ausgabe

OriginalPaper

Design of Probability Density Function Targeting Energy Efficient Network for Coalition Based WSNs

OriginalPaper

Environment Adaptive Localization Method Using Wi-Fi and Bluetooth Low Energy

OriginalPaper

A Lightweight Defense Approach to Mitigate Version Number and Rank Attacks in Low-Power and Lossy Networks

OriginalPaper

Nanoantenna with Geometric Diode for Energy Harvesting

OriginalPaper

Report Hexagonal Based Dynamic Location Finding Techniques with Sequence-Based Localization in Wireless Sensor Network

OriginalPaper

Multipath Routing to Improve Quality of Service for Video Streaming Over Mobile Ad Hoc Networks

Neuer Inhalt

    Bildnachweise