Image-based Ear Biometric Smartphone App for Patient Identification in Field Settings

Sarah Adel Bargal, Alexander Welles, Cliff R. Chan, Samuel Howes, Stan Sclaroff, Elizabeth Ragan, Courtney Johnson, Christopher Gill

2015

Abstract

We present a work in progress of a computer vision application that would directly impact the delivery of healthcare in underdeveloped countries. We describe the development of an image-based smartphone application prototype for ear biometrics. The application targets the public health problem of managing medical records at on-site medical clinics in less developed countries where many individuals do not hold IDs. The domain presents challenges for an ear biometric system, including varying scale, rotation, and illumination. It was not clear which feature descriptors would work best for the application, so a comparative study of three ear biometric extraction techniques was performed, one of which was used to develop an iOS application prototype to establish the identity of an individual using a smartphone camera image. A pilot study was then conducted on the developed application to test feasibility in naturalistic settings.

References

  1. Abate, A., Nappi, M., Riccio, D., and Andricciardi, S. (2006). Ear recognition by means of a rotation invariant descriptor. Pattern Recognition, ICPR. 18th International Conference on, 4:437-440.
  2. Abaza, A., Ross, A., Hebert, C., Harrison, M. F., and Nixon, M. S. (2013). A survey on ear biometrics. ACM Computing Surveys (CSUR) Journal, 45(2):22.
  3. Abdel-Mottaleb, M. and Zhou, J. (2005). Human ear recognition from face profile images. Advances in biometrics, pages 786-792.
  4. Azfar, R. S., Weinberg, J. L., Cavric, G., Lee-Keltner, I. A., Bilker, W. B., Gelfand, J. M., and Kovarik, C. L. (2011). HIV-positive patients in botswana state that mobile teledermatology is an acceptable method for receiving dermatology care. Journal of telemedicine and telecare, 17(6):338-340.
  5. Biometrics Metrics Report v3.0 (2012). Prepared for: U.S. Military Academy (USMA) - West Point. http://www.usma.edu/ietd/docs/ BiometricsMetricsReport.pdf.
  6. Biometrics Research Laboratory (2013). IIT Delhi Ear Database. http://www4.comp.polyu.edu.hk/ csajaykr/IITD/Database Ear.htm.
  7. Boodoo-Jahangeer, N. B. and Baichoo, S. (2013). LBPbased ear recognition. Bioinformatics and Bioengineering (BIBE), IEEE 13th International Conference on, pages 1-4.
  8. Bradski, G. (2000). Dr. Dobb's Journal of Software Tools.
  9. Chang, K., Bowyer, K., Sarkar, S., and Victor, B. (2003). Comparison and combination of ear and face images in appearance-based biometrics. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 25(9):1160-1165.
  10. Cummings, A., Nixon, M., and Carter, J. (2010). A novel ray analogy for enrollment of ear biometrics. Biometrics: Theory Applications and Systems (BTAS), Fourth IEEE International Conference on, pages 1-6.
  11. Delac, K. and Grgic, M. (2004). A survey of biometric recognition methods. Electronics in Marine, 2004. Proceedings Elmar 2004. 46th International Symposium, pages 184-193.
  12. Fahmi, A., Kodirov, E., Choi, D., Lee, G., M. F. Azli A., and Sayeed, S. (2012). Implicit authentication based on ear shape biometrics using smartphone camera during a call. Systems, Man, and Cybernetics (SMC), IEEE International Conference on, pages 2272-2276.
  13. Goode, A. (2014). Bring your own finger-how mobile is bringing biometrics to consumers. Biometric Technology Today, 2014(5):5-9.
  14. Iannarelli, A. (1989). Ear identification, forensic identification series, fremont. Paramont Publishing Company, Calif, ISBN, 10:0962317802.
  15. Kisku, D. R., Mehrota, H., Gupta, P., and Sing, J. K. (2009). SIFT-based ear recognition by fusion of detected keypoints from color similarity slice regions. Advances in Computational Tools for Engineering Applications, 2009. ACTEA'09. International Conference on, pages 380-385.
  16. Kumar, A. and Wu, C. (2012). Automated human identification using ear imaging. Pattern Recognition, 45(3):956-968.
  17. Kumar, M. (2014). Hanseatic institute of technology. cell phone-based intelligent biometrics. http://www.appropedia.org/ Cell phone-based intelligent biometrics .
  18. Kwapisz, J. R., Weiss, G. M., and Moore, S. A. (2010). Cell phone-based biometric identification. Biometrics: Theory Applications and Systems (BTAS), Fourth IEEE International Conference on, pages 1-7.
  19. Lowe, D. (1999). Object recognition from local scaleinvariant features. Computer vision. The proceedings of the seventh IEEE international conference on, 2:1150-1157.
  20. Mäenpää, T., Ojala, T., Pietikäinen, M., and Soriano, M. (2000). Robust texture classification by subsets of local binary patterns. Pattern Recognition, 2000. Proceedings. 15th International Conference on, 3:935- 938.
  21. Ojala, T., Pietikainen, M., and Harwood, D. (1996). A comparative study of texture measures with classification based on feature distributions. Pattern Recognition, 29(1):51-59.
  22. Takala, V., Ahonen, T., and Pietikäinen, M. (2005). Blockbased methods for image retrieval using local binary patterns. Image Analysis, pages 882-891.
  23. Vedaldi, A. and Fulkerson, B. (2008). VLFeat: An open and portable library of computer vision algorithms. http://www.vlfeat.org/.
  24. Wang, Y., Mu, Z., and Zeng, H. (2008). Block-based and multi-resolution methods for ear recognition using wavelet transform and uniform local binary patterns. Pattern Recognition, ICPR. 19th International Conference on, pages 1-4.
  25. Zhang, D. and Lu, G. (2002). Shape-based image retrieval using generic fourier descriptor. Signal Processing: Image Communication, 17(10):825-848.
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Paper Citation


in Harvard Style

Adel Bargal S., Welles A., R. Chan C., Howes S., Sclaroff S., Ragan E., Johnson C. and Gill C. (2015). Image-based Ear Biometric Smartphone App for Patient Identification in Field Settings . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-091-8, pages 171-179. DOI: 10.5220/0005342201710179


in Bibtex Style

@conference{visapp15,
author={Sarah Adel Bargal and Alexander Welles and Cliff R. Chan and Samuel Howes and Stan Sclaroff and Elizabeth Ragan and Courtney Johnson and Christopher Gill},
title={Image-based Ear Biometric Smartphone App for Patient Identification in Field Settings},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={171-179},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005342201710179},
isbn={978-989-758-091-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)
TI - Image-based Ear Biometric Smartphone App for Patient Identification in Field Settings
SN - 978-989-758-091-8
AU - Adel Bargal S.
AU - Welles A.
AU - R. Chan C.
AU - Howes S.
AU - Sclaroff S.
AU - Ragan E.
AU - Johnson C.
AU - Gill C.
PY - 2015
SP - 171
EP - 179
DO - 10.5220/0005342201710179