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A smart application to detect pupil for small dataset with low illumination

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Abstract

Biometric applications are very sensitive to the process because of its complexity in presenting unstructured input to the processing. The existing applications of image processing are based on the implementation of different programing segments such as image acquisition, segmentation, extraction, and final output. The proposed model is designed with 2 convolution layers and 3 dense layers. We examined the module with 5 datasets including 3 benchmark datasets, namely CASIA, UBIRIS, MMU, random dataset, and the live video. We calculated the FPR, FNR, Precision, Recall, and accuracy of each dataset. The calculated accuracy of CASIA using the proposed system is 82.8%, for UBIRIS is 86%, MMU is 84%, and the random dataset is 84%. On live video with low resolution, calculated accuracy is 72.4%. The proposed system achieved better accuracy compared to existing state-of-the-art systems.

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References

  1. Daugman J (2003) The importance of being random: statistical principles of iris recognition. Pattern Recogn 36(2):279–291

    Article  Google Scholar 

  2. Daugman J (2009) How iris recognition works. In: The essential guide to image processing, Academic Press, pp 715–739

  3. Boles WW, Boashash B (1998) A human identification technique using images of the iris and wavelet transform. IEEE Trans Signal Process 46(4):1185–1188

    Article  Google Scholar 

  4. Masek L (2003) Matlab source code for a biometric identification system based on iris patterns. http://people.csse.uwa.edu.au/pk/studentprojects/libor/

  5. Jamal I, Akram MU, Tariq A (2012) Retinal image preprocessing: background and noise segmentation. Telkomnika 10(3):537–544

    Article  Google Scholar 

  6. Sharma N, Ray AK, Sharma S, Shukla KK, Pradhan S, Aggarwal LM (2008) Segmentation and classification of medical images using texture-primitive features: application of BAM-type artificial neural network. J Med Phys Assoc Med Phys India 33(3):119

    Google Scholar 

  7. Ma L, Tan T, Wang Y, Zhang D (2003) Personal identification based on iris texture analysis. IEEE Trans Pattern Anal Mach Intell 25(12):1519–1533

    Article  Google Scholar 

  8. Suganthy M, Ramamoorthy P (2012) Principal component analysis based feature extraction, morphological edge detection and localization for fast iris recognition. J Comput Sci 8(9):1428

    Article  Google Scholar 

  9. Najafi M, Ghofrani S (2011) 7 Iris recognition based on using ridgelet and curvelet transform

  10. Radman A, Jumari K, Zainal N (2013) Fast and reliable iris segmentation algorithm. IET Image Proc 7(1):42–49

    Article  Google Scholar 

  11. Tsai H-S, Hung W-L, Yang M-S (212) A robust kernel-based fuzzy c-means algorithm by incorporating suppressed and magnified membership for MRI image segmentation. In: International conference on artificial intelligence and computational intelligence, Springer, Berlin, Heidelberg, pp 744–754

  12. Chinsatit W, Saitoh T (2017) CNN-based pupil center detection for wearable gaze estimation system. Appl Comput Intell Soft Comput 2017

  13. Jan F, Usman I (2014) Iris segmentation for visible wavelength and near infrared eye images. Optik 125(16):4274–4282

    Article  Google Scholar 

  14. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440

  15. Liu N, Li H, Zhang M, Liu J, Sun Z, Tan T (2016) Accurate iris segmentation in non-cooperative environments using fully convolutional networks. In: 2016 international conference on biometrics (ICB), IEEE, pp 1–8

  16. Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, Cham, pp 234–241

  17. Abbasi A, Khan M (2016) Iris-pupil thickness based method for determining age group of a person. Int Arab J Inf Technol 13(6)

  18. Ahmad S, Fuller B (2018) Unconstrained iris segmentation using convolutional neural networks. In: Asian conference on computer vision, Springer, Cham, pp 450–466

  19. Tobji R, Di W, Ayoub N (2019) FMnet: iris segmentation and recognition by using fully and multi-scale CNN for biometric security. Appl Sci 9(10):2042

    Article  Google Scholar 

  20. Bazrafkan S, Thavalengal S, Corcoran P (2018) An end to end deep neural network for iris segmentation in unconstrained scenarios. Neural Netw 106:79–95

    Article  Google Scholar 

  21. Pathak M, Bairagi V, Srinivasu N. Multimodal eye biometric system based on contour based E-CNN and multi algorithmic feature extraction using SVBF matching

  22. Han SY, Kwon HJ, Kim Y, Cho NI (2020) Noise-robust pupil center detection through CNN-based segmentation with shape-prior loss. IEEE Access 8:64739–64749

    Article  Google Scholar 

  23. Lee YW, Kim KW, Hoang TM, Arsalan M, Park KR (2019) Deep residual CNN-based ocular recognition based on rough pupil detection in the images by NIR camera sensor. Sensors 19(4):842

    Article  Google Scholar 

  24. Pathak M, Srinivasu N, Bairagi V (2019) Effective segmentation of sclera, iris and pupil in noisy eye images. Telkomnika 17(5)

  25. Lozej J, Meden B, Struc V, Peer P (2018) End-to-end iris segmentation using U-Net. In: 2018 IEEE international work conference on bioinspired intelligence (IWOBI), IEEE, pp 1–6

  26. Nguyen K, Fookes C, Ross A, Sridharan S (2017) Iris recognition with off-the-shelf CNN features: a deep learning perspective. IEEE Access 6:18848–18855

    Article  Google Scholar 

  27. Salve SS, Narote SP (2016) Iris recognition using SVM and ANN. In: 2016 international conference on wireless communications, signal processing and networking (WiSPNET), IEEE, pp 474–478

  28. Das A, Pal U, Ballester MAF, Blumenstein M (2014) A new efficient and adaptive sclera recognition system. In: 2014 IEEE symposium on computational intelligence in biometrics and identity management (CIBIM), IEEE, pp 1–8

  29. Li D, Winfield D, Parkhurst DJ (2005) Starburst: a hybrid algorithm for video-based eye tracking combining feature-based and model-based approaches. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05)-workshops, IEEE, pp 79–79

  30. Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395

    Article  MathSciNet  Google Scholar 

  31. Fuhl W, Santini T, Kasneci G, Kasneci E (2016) Pupilnet: convolutional neural networks for robust pupil detection. Preprint arXiv:1601.04902

  32. Arsalan M, Hong HG, Naqvi RA, Lee MB, Kim MC, Kim DS, Kim CS, Park KR (2017) Deep learning-based iris segmentation for iris recognition in visible light environment. Symmetry 9(11):263

    Article  Google Scholar 

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Authors and Affiliations

  1. Lovely Professional University, Phagwara, Punjab, India

    A. Swathi &  Aarti

  2. Sreyas Institute of Engineering and Technology, Hyderabad, India

    Sandeep Kumar

Authors
  1. A. Swathi
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  2. Aarti
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  3. Sandeep Kumar
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Correspondence to A. Swathi.

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Swathi, A., Aarti & Kumar, S. A smart application to detect pupil for small dataset with low illumination. Innovations Syst Softw Eng 17, 29–43 (2021). https://doi.org/10.1007/s11334-020-00382-3

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  • DOI: https://doi.org/10.1007/s11334-020-00382-3

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