http://iet.metastore.ingenta.com
1887

Super resolution and recognition of long range captured multi-frame iris images

Super resolution and recognition of long range captured multi-frame iris images

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Biometrics — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

In this study, a framework to super resolve and recognise the long range captured iris polar images is proposed. The proposed framework consists of best frame selection algorithm, modified diamond search algorithm, Gaussian process regression (GPR) based and enhanced iterated back projection (EIBP)-based super-resolution approach, fuzzy entropy-based feature selector and neural network (NN) classifier. The framework uses linear kernel co-variance function in local patch-based GPR and EIBP algorithms and it super resolves the iris images depending on the contents of the patches, without an external dataset. NN classifier classifies the iris images by using features extracted using discrete cosine transform domain based no-reference image quality assessment model, Gray level co-occurrence matrix, Hu seven moments and statistical features. The framework is tested using CASIA long range iris database by comparing and analysing the peak signal-to-noise ratio, structural similarity index matrix and visual information fidelity in pixel domain of proposed algorithms with Yang and Nguyen framework. The results demonstrate that the proposed framework is well suited for recognition of iris images captured at a long distance.

References

    1. 1)
      • 1. Nguyen, K., Fookes, C., Sridharan, S., et al: ‘Robust mean super- resolution for less cooperative NIR iris recognition at distance and on the move’. Symp. on Information and Communication Technology, 2010.
    2. 2)
      • 2. Chaudhuri, S.: ‘Super resolution imaging’ (Kluwer Academic Publishers, 2002).
    3. 3)
      • 3. Park, S., Park, M.K., Kang, M.G., et al: ‘Super-resolution image reconstruction: technical overview’, IEEE Signal Process. Mag., 2003, 20, pp. 2136.
    4. 4)
      • 4. Kohler, T., Brost, A., Mogalle, K., et al: ‘Multi-frame super-resolution with quality self-assessment for retinal fundus videos’, Med. Image Comput. Comput.-Assist. Interv., 2014, 8673, pp. 650657.
    5. 5)
      • 5. Tam, W.S., Kok, C.W., Siu, W.C.: ‘Modified edge-directed interpolation for images’, J. Electron. Imaging, 2010, 19, (1), pp. 120, Article ID 013011.
    6. 6)
      • 6. Sun, J., Sun, J., Xu, Z., et al: ‘Image super-resolution using gradient profile prior’. IEEE Conf. on Computer Vision and Pattern Recognition, 2008.
    7. 7)
      • 7. Xu, H., Zhai, G., Yang, X., et al: ‘Single image super-resolution with detail enhancement based on local fractal analysis of gradient’, IEEE Trans. Circuits Syst., 2013, 23, pp. 17401754.
    8. 8)
      • 8. Wang, L., Meng, G., Wu, H., et al: ‘Edge-directed single-image super-resolution via adaptive gradient magnitude self-interpolation’, IEEE Trans. Circuits Syst. Video Technol., 2013, 23, pp. 12891299.
    9. 9)
      • 9. Zhang, K., Gao, X., Tao, D., et al: ‘Single image super-resolution with non-local means and steering kernel regression’, IEEE Trans. Image Process., 2012, 21, pp. 4544455.
    10. 10)
      • 10. Gao, X., Wang, Q., Li, X., et al: ‘Zernike-moment-based image super resolution’, IEEE Trans. Image Process., 2011, 20, pp. 273847.
    11. 11)
      • 11. Zhang, K., Gao, X., Tao, D., et al: ‘Single image super-resolution with multiscale similarity learning’, IEEE Trans. Neural Netw. Learn. Syst., 2013, 24, pp. 164859.
    12. 12)
      • 12. Fahmy, G.: ‘Super-resolution construction of iris images from a visual low resolution face video’. Int. Symp. on Signal Processing and its Applications, 2007.
    13. 13)
      • 13. Zhang, K., Gao, X., Tao, D., et al: ‘Single image super-resolution with multi-scale similarity learning’, IEEE Trans. Neural Netw. Learn. Syst., 2013, 24, pp. 164859.
    14. 14)
      • 14. Wang, N., Tao, D., Gao, X., et al: ‘Comprehensive survey to face hallucination’, Int. J. Comput. Vis., 2014, 106, pp. 930.
    15. 15)
      • 15. Zhang, K., Tao, D., Gao, X., et al: ‘Learning multiple linear mappings for efficient single image super-resolution’, IEEE Trans. Image Process., 2015, 24, pp. 846861.
    16. 16)
      • 16. Shin, K.Y., Park, K.R., Kang, B.J., et al: ‘Super-resolution method based on multiple multi-layer perceptrons for iris recognition’. Int. Conf. on Ubiquitous Information Technologies Applications, 2009.
    17. 17)
      • 17. Huang, J., Ma, L., Tan, T., et al: ‘Learning based resolution enhancement of iris images’. British Machine Vision Conf., 2003.
    18. 18)
      • 18. Park, U., Jillela, R.R., Ross, A., et al: ‘Periocular biometrics in the visible spectrum’, IEEE Trans. Inf. Forensics Secur., 2011, 6, pp. 96106.
    19. 19)
      • 19. Viola, P.: ‘Rapid object detection using a boosted cascade of simple features’. IEEE Conf. on Computer Vision and Pattern Recognition, 2001.
    20. 20)
      • 20. Deshpande, A., Patavardhan, P.: ‘Segmentation and quality analysis of long range captured iris image’, ICTACT J. Image Video Process., 2016, 6, pp. 12801283.
    21. 21)
      • 21. Daugman, J.: ‘High confidence visual recognition of persons by a test of statistical independence’, IEEE Trans. Pattern Anal. Mach. Intell., 1993, 15, pp. 11481161.
    22. 22)
      • 22. Masek, L.: ‘Recognition of human iris patterns for biometric identification’, 2003.
    23. 23)
      • 23. Jia, H.: ‘A new cross diamond search algorithm for block motion estimation’. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, 2004.
    24. 24)
      • 24. Haghighat, , Aghagolzadeha, A., Seyedarabia, H., et al: ‘Multi-focus image fusion for visual sensor networks in DCT domain’, Comput. Electr. Eng., 2011, 37, pp. 789797.
    25. 25)
      • 25. He, H., Siu, W.-C.: ‘Single image super-resolution using Gaussian process regression’. IEEE Conf. Proc. on Pattern Recognition, 2011.
    26. 26)
      • 26. Georgis, , Lentaris, G., Reisis, D., et al: ‘Single-image super-resolution using low complexity adaptive iterative back-projection’. IEEE Conf. on Digital Signal Proc., 2013.
    27. 27)
      • 27. Irani, M.: ‘Super resolution from image sequences’. IEEE Conf. on Pattern Recognition, 1990.
    28. 28)
      • 28. Saad, M.A.: ‘DCT statistics model-based blind image quality assessment’. IEEE Int. Conf. on Image Processing, 2011.
    29. 29)
      • 29. Huan, J., Parris, M., Lee, J., et al: ‘Scalable FPGA-based architecture for DCT computation using dynamic partial reconfiguration’, ACM Trans. Embed. Comput. Syst., 2009, 9, pp. 118.
    30. 30)
      • 30. Haralick, R.M., Shanmugam, K., Dinstein, I., et al: ‘Textural features for image classification’, IEEE Trans. Syst. Man Cybern., 1973, 3, pp. 610621.
    31. 31)
      • 31. Hu, M.: ‘Visual pattern recognition by moment invariants’, IRE Trans. Inf. Theory, 1962, 8, pp. 179187.
    32. 32)
      • 32. Mezei, J., Morente-Molinera, J.A., Carlsson, C., et al: ‘Feature selection with fuzzy entropy to find similar cases’, Adv. Trends Soft Comput., 2014, 312, pp. 383390.
    33. 33)
      • 33. Carlsson, C., Heikkilä, M., Mezei, J., et al: ‘Fuzzy entropy used for predictive analytics’. IEEE Conf. on Fuzzy Systems, 2015.
    34. 34)
      • 34. Shie, J.-D., Chen, S.-M.: ‘Feature subset selection based on fuzzy entropy measures for handling classification problems’, Springer Appl. Intell., 2008, 28, pp. 6982.
    35. 35)
      • 35. Nozaki, K., Ishibuchi, H., Tanaka, H., et al: ‘Adaptive fuzzy rule-based classification systems’, IEEE Trans. Fuzzy Syst., 1996, 4, pp. 238250.
    36. 36)
      • 36. Abiyev, R.H.: ‘Neural network based biometric personal identification with fast iris segmentation’, J. Control Autom. Syst., 2009, 7, pp. 1723.
    37. 37)
      • 37. CASIA Iris Image Database, http://biometrics.idealtest.org/.
    38. 38)
      • 38. Yang, J., Wang, Z., Lin, Z., et al: ‘Coupled dictionary training for image super-resolution’, IEEE Trans. Image Process., 2012, 21, pp. 34673478.
    39. 39)
      • 39. Thanh, N., Sridharan, S., Denman, S., et al: ‘Feature-domain super-resolution framework for Gabor-based face and iris recognition’. IEEE Int. Conf. in Computer Vision and Pattern Recognition, 2012.
    40. 40)
      • 40. Wang, : ‘Image quality assessment: from error visibility to structural similarity’. IEEE Proc. on Image Processing, 2004.
    41. 41)
      • 41. Sheikh, , Bovik, A.C., de Veciana, G., et al: ‘An information fidelity criterion for image quality assessment using natural scene statistics’, IEEE Trans. Image Process., 2005, 14, pp. 21172128.
    42. 42)
      • 42. Lukes, , Fliegel, K., Klíma, M., et al: ‘Performance evaluation of image quality metrics with respect to their use for super-resolution enhancement’, IEEE Fifth Int. Workshop on Quality of Multimedia Experience (QoMEX), 2013.
    43. 43)
      • 43. Zhou, : ‘Evaluating the quality of super-resolved images for face recognition’. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 2008.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-bmt.2016.0075
Loading

Related content

content/journals/10.1049/iet-bmt.2016.0075
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address