Skip to main content

Identity Verification Using Biometrics in Smart-Cities

  • Chapter
  • First Online:
  • 578 Accesses

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Abstract

Biometrics suggests a smart solution to keep the city safe. Installing a biometrics app on a mobile device facilitates identity recognition and verification instantaneously. Current work explores an authentication algorithm to address requirements of such memory restricted apps. A potential portion of periocular region, known as lower central periocular region, is examined to attain unconstrained authentication coupled with benefits of reduced template size. A novel computationally efficient feature extraction approach is employed over the region of interest using an efficient variation of conventional local binary pattern. The technique computes texture patterns over a dominant bit-plane, alternative to employing entire intensity image itself. Construction of the dominant bit-plane prior to feature extraction significantly simplifies operations required for texture pattern computations. The proposed methodology is tested on benchmark UBIRISv2 database and periocular images retrieved from high and low resolution imaging devices. Experimental results show an attainment up to 99.5% authentication accuracy in an unconstrained environment.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   129.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Abbreviations

FAR:

False accept rate

FRR:

False error rate

IoT:

Internet of Things

LBP:

Local binary patterns

LCPR:

Lower central periocular region

RAM:

Random access memory

ROC:

Receiver operating characteristics

ROI:

Region of interest

SSIM:

Structural similarity index

References

  1. Adams, J., Woodard, D. L., Dozier, G., Miller, P., Bryant, K., & Glenn, G. (2010). Genetic-based type II feature extraction for periocular biometric recognition: Less is more. In 20th International Conference on Pattern Recognition. Piscataway: IEEE.

    Google Scholar 

  2. Ahonen, T., Hadid, A., & Pietikäinen, M. (2006). Face description with local binary patterns: Application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12), 2037–2041.

    Article  Google Scholar 

  3. Ahonen, T., & Pietikäinen, M. (2008). A framework for analyzing texture descriptors. In VISAPP 2008: Proceedings of the Third International Conference on Computer Vision Theory and Applications (Vol. 1, pp. 507–512). Setubal: Institute for Systems and Technologies of Information, Control and Communication.

    Google Scholar 

  4. Akanksha, J., Abhishek, G., Renu, S., Ashutosh, S., & Zia., S. (2014). Periocular recognition based on Gabor and Parzen PNN. In 2014 IEEE International Conference on Image Processing (ICIP) (pp. 4977–4981). Piscataway: IEEE.

    Google Scholar 

  5. Alabady, S. A., & Al-Turjman, F. (2018). Low complexity parity check code for futuristic wireless networks applications. IEEE Access Journal, 6(1), 18398–18407.

    Article  Google Scholar 

  6. Alabady, S. A., & Al-Turjman, F. (2018). A novel security model for cooperative virtual networks in the IoT era. International Journal of Parallel Programming, 1–16.

    Google Scholar 

  7. Al-Turjman, F., & Alturjman, S. (2018). Confidential smart-sensing framework in the IoT era. The Journal of Supercomputing, 74(10), 5187–5198.

    Article  Google Scholar 

  8. Al-Turjman, F., & Alturjman, S. (2018). Context-sensitive access in industrial internet of things (IIoT) healthcare applications. IEEE Transactions on Industrial Informatics, 14(6), 2736–2744.

    Article  Google Scholar 

  9. Al-Turjman, F., Hasan, M. Z., & Al-Rizzo, H. (2018). Task scheduling in cloud-based survivability applications using swarm optimization in IoT. Transactions on Emerging Telecommunications Technologies, e3539.

    Google Scholar 

  10. Ambika, D. R., Radhika, K. R., & Seshachalam, D. (2016). Periocular authentication based on fem using Laplace–Beltrami eigenvalues. Pattern Recognition Journal, 50(C), 178–194.

    Article  Google Scholar 

  11. Bae, S. H., & Kim, M. (2015). A novel SSIM index for image quality assessment using a new luminance adaptation effect model in pixel intensity domain. In Visual communications and image processing (VCIP). Piscataway: IEEE.

    Google Scholar 

  12. Bakshi, S., Sa, P. K., & Majhi, B. (2013). Optimized periocular template selection for human recognition. BioMed Research International, 2013, 14. https://doi.org/10.1155/2013/481431.

    Article  Google Scholar 

  13. Bakshi, S., Sa, P. K., & Majhi, B. (2015). A novel phase-intensive local pattern for periocular recognition under visible spectrum. Biocybernetics and Biomedical Engineering, 35(1), 30–44.

    Article  Google Scholar 

  14. Bharadwaj, S., Bhatt, H., Vatsa, M., & Singh, R. (2010). Periocular biometrics: When iris recognition fails. In Fourth IEEE International Conference on Biometrics Compendium (pp. 1–6). Piscataway: IEEE.

    Google Scholar 

  15. Boddeti, V. N., Smereka, J. M., & Kumar, B. V. (2011). A comparative evaluation of iris and ocular recognition methods on challenging ocular images. In 2011 International Joint Conference on Biometrics (IJCB) (pp. 1–8). Piscataway: IEEE

    Google Scholar 

  16. Chan, T., & Vese, L. (2001). Active contour without edges. IEEE Transactions on Image Processing, 10, 266–277.

    Article  Google Scholar 

  17. Hollingsworth, K., Bowyer, K. W., & Flynn, P. J. (2010). Identifying useful features for recognition in near-infrared periocular images. In Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS) (pp. 1–8). Piscataway: IEEE

    Google Scholar 

  18. Hollingsworth, K., Darnell, S. S., Miller, P. E., Woodard, D. L., Bowyer, K. W., & Flynn, P. J. (2012). Human and machine performance on periocular biometrics under near-infrared light and visible light. IEEE Transactions on Information Forensics and Security, 7(2), 588–601.

    Article  Google Scholar 

  19. Juefei, X., & Savvides, M. (2012). Unconstrained periocular biometric acquisition and recognition using COTS PTZ camera for uncooperative and non-cooperative subjects. In IEEE Workshop on Applications of Computer Vision, WACV (pp. 201–208). Piscataway: IEEE.

    Google Scholar 

  20. Juefei, X., & Savvides, M. (2014). Subspace based discrete transform encoded local binary patterns representations for robust periocular matching on NIST’s face recognition grand challenge. IEEE Transactions on Image Processing, 23, 3490–3505. https://doi.org/10.1109/TIP.2014.2329460.

    Article  MathSciNet  Google Scholar 

  21. Kanumuri, T., Dewal, M. L., & Anand, R. S. (2014). Progressive medical image coding using binary wavelet transforms. Signal, Image and Video Processing, 8(5), 883–899.

    Article  Google Scholar 

  22. Li, J., Du, Q., & Sun, C. (2009). An improved box-counting method for image fractal dimension estimation. Pattern Recognition Journal, 42(11), 2460–2469.

    Article  Google Scholar 

  23. López, M. B., Nieto, A., Boutellier, J., Hannuksela, J., & Silvén, O. (2014). Evaluation of real-time LBP computing in multiple architectures. Journal of Real-Time Image Processing, 1–22.

    Google Scholar 

  24. Lyle, J. R., Miller, P. E., Pundlik, S. J., & Woodard, D. L. (2010). Soft biometric classification using periocular region features. In Fourth IEEE International Conference on Biometrics Compendium (pp. 1–7). Piscataway: IEEE.

    Google Scholar 

  25. Miller, P. E., Lyle, J. R., Pundlik, S. J., & Woodard, D. L. (2010). Performance evaluation of local appearance based periocular recognition. In Fourth IEEE International Conference on Biometrics Compendium (pp. 1–6). Piscataway: IEEE.

    Google Scholar 

  26. Ojala, T., Pietikainen, M., & Harwood, D. (1994). Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. In International conference on Pattern Recognition (ICPR) (Vol. 1, pp. 582–585). Piscataway: IEEE.

    Google Scholar 

  27. Padole, N. C., & Proença, H. (2012). Periocular recognition: Analysis of performance degradation factors. In: 5th IAPR International Conference on Biometrics (ICB) (pp. 439–445). Piscataway: IEEE. https://doi.org/10.1109/ICB.2012.6199790.

    Chapter  Google Scholar 

  28. Park, U., Arun, R., & Jain, A. K. (2009). Periocular biometrics in the visible spectrum: A feasibility study. In IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, BTAS (Vol. 6, pp. 28–30). Piscataway: IEEE.

    Google Scholar 

  29. Park, U., Ross, A., & Jain, A. K. (2011). Periocular biometrics in the visible spectrum. In IEEE Transactions on Information Forensics and Security. Piscataway: IEEE.

    Google Scholar 

  30. 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.

    Article  Google Scholar 

  31. Proença, H., Neves, J. C., & Santos, G. (2014). Segmenting the periocular region using a hierarchical graphical model fed by texture/shape information and geometrical constraints. In IEEE International Joint Conference on Biometrics (IJCB). Piscataway: IEEE.

    Google Scholar 

  32. Rousson, M., & Paragios, N. (2002). Shape priors for level set representations. In European Conference on Computer Vision (pp. 78–92). Berlin: Springer.

    Google Scholar 

  33. Seok, O. B., Kangrok, O., & Ann, T. K. (2012). On projection-based methods for periocular identity verification. In Industrial Electronics and Applications (ICIEA) (pp. 871–876). Piscataway: IEEE.

    Google Scholar 

  34. Sharma, A., Verma, S., Vatsa, M., & Singh, R. (2014). On cross spectral periocular recognition. In 2014 IEEE International Conference on Image Processing (pp. 5007–5011). Piscataway: IEEE.

    Chapter  Google Scholar 

  35. Uzair, M., Mahmood, A., Mian, A., & McDonald, C. (2015). Periocular region-based person identification in the visible, infrared and hyperspectral imagery. Neurocomputing, 149, 854–867

    Article  Google Scholar 

  36. Vazquez-Fernandez, E., Garcia-Pardo, H., Gonzalez-Jimenez, D., & Perez-Freire, L. (2011). Built-in face recognition for smart photo sharing in mobile devices. In IEEE International Conference on In Multimedia and Expo (ICME) (pp. 1–4). Piscataway: IEEE

    Google Scholar 

  37. Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transaction on Image Processing, 13(4), 600–612.

    Article  Google Scholar 

  38. Woodard, D. L., Pundlik, S. J., Lyle, J. R., & Miller, P. E. (2010). Periocular region appearance cues for biometric identification. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPRW) (pp. 162–169). Piscataway: IEEE.

    Google Scholar 

  39. Woodard, D., Pundlik, S., Miller, P., Jillela, R., & Ross, A. (2010). On the fusion of periocular and iris biometrics in non-ideal imagery. In 20th International Conference on Pattern Recognition. Piscataway: IEEE.

    Google Scholar 

  40. Woodard, D., Pundlik, S., Miller, P., & Lyle, J. R. (2011). Appearance-based periocular features in the context of face and non-ideal iris recognition. In Signal, Image and Video Processing (Vol. 5). Berlin: Springer.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to D. R. Ambika .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ambika, D.R., Radhika, K.R., Seshachalam, D. (2020). Identity Verification Using Biometrics in Smart-Cities. In: Al-Turjman, F. (eds) Smart Cities Performability, Cognition, & Security. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-14718-1_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-14718-1_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-14717-4

  • Online ISBN: 978-3-030-14718-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics