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Iris tissue recognition based on GLDM feature extraction and hybrid MLPNN-ICA classifier

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Abstract

The use of iris tissue for identification is an accurate and reliable system for identifying people. This method consists of four main processing stages, namely segmentation, normalization, feature extraction, and matching. In this study, a new method of feature extraction and classification based on gray-level difference method and hybrid MLPNN-ICA classifier is proposed. For experimental results, our study is implemented on CASIA-Iris V3 dataset and UCI machine learning repository datasets.

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References

  1. Saba T, Altameem A (2013) Analysis of vision based systems to detect real time goal events in soccer videos. Appl Artif Intell 27:656–667

    Article  Google Scholar 

  2. Li C, Zhou W, Yuan S (2015) Iris recognition based on a novel variation of local binary pattern. Vis Comput 31:1419–1429

    Article  Google Scholar 

  3. Colores-Vargas JM, García-Vázquez M, Ramírez-Acosta A, Pérez-Meana H, Nakano-Miyatake M (2013) Video images fusion to improve iris recognition accuracy in unconstrained environments. In: Mexican conference on pattern recognition. Springer, Berlin, pp 114–125

  4. Rahulkar AD, Waghmare LM, Holambe RS (2014) A new approach to the design of hybrid finer directional wavelet filter bank for iris feature extraction and classification using k-out-of-n: a post-classifier. Pattern Anal Appl 17:529–547

    Article  MathSciNet  Google Scholar 

  5. Sun Z, Tan T, Wang Y (2004) Robust encoding of local ordinal measures: a general framework of iris recognition. In: International workshop on biometric authentication. Springer, Berlin, pp 270–282

  6. Alvarez-Betancourt Y, Garcia-Silvente M (2014) An overview of iris recognition: a bibliometric analysis of the period 2000–2012. Scientometrics 101:2003–2033

    Article  Google Scholar 

  7. Kong WK, Zhang D (2001) Accurate iris segmentation based on novel reflection and eyelash detection model. In: Proceedings of 2001 international symposium on intelligent multimedia, video and speech processing: IEEE, pp 263–266

  8. Samanta S, Ahmed SS, Salem MAMM, Nath SS, Dey N, Chowdhury SS (2015) Haralick features based automated glaucoma classification using back propagation neural network. In: Proceedings of the 3rd international conference on frontiers of intelligent computing: theory and applications (FICTA) 2014. Springer, Cham, pp 351–358

  9. Othman N, Houmani N, Dorizzi B (2015) Quality-based super resolution for degraded iris recognition. In: Pattern recognition applications and methods. Springer International Publishing, pp 285–300

  10. Deshmukh M, Prasad MV (2015) Partial segmentation and matching technique for iris recognition. In: Computational intelligence in data mining. Springer India, pp 77–86

  11. Khalighi S, Pak F, Tirdad P, Nunes U (2015) Iris recognition using robust localization and non-subsampled contourlet based features. J Signal Process Syst 81:111–128

    Article  Google Scholar 

  12. Bansal A, Agarwal R, Sharma RK (2015) Determining diabetes using iris recognition system. Int J Diabetes Dev Ctries 35:432–438

    Article  Google Scholar 

  13. Wang N, Li Q, El-Latif AAA, Zhang T, Niu X (2014) Toward accurate localization and high recognition performance for noisy iris images. Multimed Tools Appl 71:1411–1430

    Article  Google Scholar 

  14. Wang Y, Tan T, Jain AK (2003) Combining face and iris biometrics for identity verification. In: International conference on audio-and video-based biometric person authentication. Springer, Berlin, pp 805–813

  15. Kordjazi N, Rahati S (2012). ait recognition for human identification using ensemble of LVQ neural networks. In: 2012 International conference on biomedical engineering (ICoBE). IEEE, pp 180–185

  16. Ahmadi N, Nilashi M (2018) Iris texture recognition based on multilevel 2-D Haar wavelet decomposition and Hamming distance approach. J Soft Comput Decis Support Syst 5(3):16–20

    Google Scholar 

  17. Jain YK, Verma MK (2012) Comparison of phase only correlation and neural network for iris recognition. Int J Comput Sci Issues 1:165–171

    Google Scholar 

  18. Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: 2007 IEEE congress on evolutionary computation. IEEE, pp 4661–4667

  19. Niknam T, Fard ET, Pourjafarian N, Rousta A (2011) An efficient hybrid algorithm based on modified imperialist competitive algorithm and K-means for data clustering. Eng Appl Artif Intell 24:306–317

    Article  Google Scholar 

  20. Qian P, Xi C, Xu M, Jiang Y, Su KH, Wang S, Jr RFM (2018) SSC-EKE: semi-supervised classification with extensive knowledge exploitation. Inf Sci 422:51–76

    Article  MathSciNet  Google Scholar 

  21. Belkin M, Niyogi P, Sindhwani V (2006) Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res 7:2399–2434

    MathSciNet  MATH  Google Scholar 

  22. Ahmadi N, Akbarizadeh G (2018) Hybrid robust iris recognition approach using iris image pre-processing, two-dimensional gabor features and multi-layer perceptron neural network/PSO. IET Biom 7(2):153–162

    Article  Google Scholar 

  23. Burge M, Burger W (1996) Ear biometrics. In: Jain A, Bolle R, Pankanti S (eds) biometrics. Springer, Boston, pp 273–285

    Google Scholar 

  24. Ahmadi N, Akbarizadeh G (2015) Iris recognition system based on canny and LoG edge detection methods. J Soft Comput Decis Support Syst 2(4):26–30

    Google Scholar 

  25. Davida GI, Frankel Y, Matt BJ (1998) On enabling secure applications through off-line biometric identification. In: sp. IEEE, p 0148

  26. Ahmadi N, Akbarizadeh G (2016) A review of iris recognition based on biometric technologies. Transylv Rev 24(4):151–163

    Google Scholar 

  27. Weszka JS, Dyer CR, Rosenfeld A (1976) A comparative study of texture measures for terrain classification. IEEE Trans Syst Man Cybern 4:269–285

    Article  Google Scholar 

  28. Graves A, Fernández S, Gomez F, Schmidhuber J (2006) Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In: Proceedings of the 23rd international conference on Machine learning. ACM, pp 369–376

  29. El-Bakry HM (2002) Human Iris detection using fast cooperative modular neural nets and image decomposition. Mach Graph Vis Int J 11(4):499–512

    Google Scholar 

  30. Russo A, Raischel F, Lind PG (2013) Air quality prediction using optimal neural networks with stochastic variables. Atmos Environ 79:822–830

    Article  Google Scholar 

  31. Database of Human Iris. Institute of Automation of Chinese Academy of Sciences (CASIA). Found on the Web page, http://www.cbsr.ia.ac.cn/english/IristissueDatabase.asp. Accessed 1 June 2017

  32. Murphy PM, Aha DW (1994) UCI Repository of machine learning databases. The University of California, Department of Information and Computer Science, Irvine. http://www.ics.uci.edu/~mlearn/MLRepository.html. Accessed 1 June 2017

  33. Liam LW, Chekima A, Fan LC, Dargham JA (2002) Iris recognition using self-organizing neural network. In: Student conference on IEEE research and development. SCOReD 2002, pp 169–172

  34. Nabti M, Bouridane A (2008) An effective and fast iris recognition system based on a combined multiscale feature extraction technique. Pattern Recogn 41:868–879

    Article  Google Scholar 

  35. Chouhan R, Jha RK, Biswas PK (2012) Wavelet-based contrast enhancement of dark images using dynamic stochastic resonance. In: Proceedings of the 8th Indian conference on computer vision, graphics and image processing. ACM, p 73

  36. Abhyankar A, Schuckers S (2010) Novel biorthogonal wavelet based iris recognition for robust biometric system. Int J Comput Theory Eng 2:233

    Article  Google Scholar 

  37. Hajari K, Gawande U, Golhar Y (2016) Neural network approach to iris recognition in noisy environment. Procedia Comput Sci 78:675–682

    Article  Google Scholar 

  38. Sahmoud SA, Abuhaiba IS (2013) Efficient iris segmentation method in unconstrained environments. Pattern Recogn 46:3174–3185

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge the financial support from the Shahid Chamran University of Ahvaz under Grant Number 96/3/02/16670. Appreciation also goes to the anonymous reviewers whose comments helped us to improve the manuscript.

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

    Neda Ahmadi

  2. Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

    Gholamreza Akbarizadeh

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  1. Neda Ahmadi
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  2. Gholamreza Akbarizadeh
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Correspondence to Gholamreza Akbarizadeh.

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Ahmadi, N., Akbarizadeh, G. Iris tissue recognition based on GLDM feature extraction and hybrid MLPNN-ICA classifier. Neural Comput & Applic 32, 2267–2281 (2020). https://doi.org/10.1007/s00521-018-3754-0

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  • DOI: https://doi.org/10.1007/s00521-018-3754-0

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