Advertisement

Identity Verification Using Biometrics in Smart-Cities

  • D. R. AmbikaEmail author
  • K. R. Radhika
  • D. Seshachalam
Chapter
Part of the EAI/Springer Innovations in Communication and Computing book series (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.

Acronyms

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. 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. 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.CrossRefGoogle Scholar
  3. 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. 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. 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.CrossRefGoogle Scholar
  6. 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. 7.
    Al-Turjman, F., & Alturjman, S. (2018). Confidential smart-sensing framework in the IoT era. The Journal of Supercomputing, 74(10), 5187–5198.CrossRefGoogle Scholar
  8. 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.CrossRefGoogle Scholar
  9. 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. 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.CrossRefGoogle Scholar
  11. 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. 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.CrossRefGoogle Scholar
  13. 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.CrossRefGoogle Scholar
  14. 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. 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: IEEEGoogle Scholar
  16. 16.
    Chan, T., & Vese, L. (2001). Active contour without edges. IEEE Transactions on Image Processing, 10, 266–277.CrossRefGoogle Scholar
  17. 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: IEEEGoogle Scholar
  18. 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.CrossRefGoogle Scholar
  19. 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. 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.MathSciNetCrossRefGoogle Scholar
  21. 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.CrossRefGoogle Scholar
  22. 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.CrossRefGoogle Scholar
  23. 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. 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. 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. 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. 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.CrossRefGoogle Scholar
  28. 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. 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. 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.CrossRefGoogle Scholar
  31. 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. 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. 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. 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.CrossRefGoogle Scholar
  35. 35.
    Uzair, M., Mahmood, A., Mian, A., & McDonald, C. (2015). Periocular region-based person identification in the visible, infrared and hyperspectral imagery. Neurocomputing, 149, 854–867CrossRefGoogle Scholar
  36. 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: IEEEGoogle Scholar
  37. 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.CrossRefGoogle Scholar
  38. 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. 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. 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

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringBMS College of EngineeringBengaluruIndia
  2. 2.Department of Information Science EngineeringBMS College of EngineeringBengaluruIndia

Personalised recommendations