Biometrics for Biomedical Applications

Part of the Studies in Computational Intelligence book series (SCI, volume 606)


This chapter focuses on the emerging applications of biometrics in biomedical and health care solutions. It includes surveys of recent pilot projects, involving new sensors of biometric data and new applications of human physiological and behavioral biometrics. It also shows the new and promising horizons of using biometrics in natural and contactless control interfaces for surgical control, rehabilitation and accessibility.


Facial Expression Face Recognition Dynamic Time Warping Severe Acute Respiratory Syndrome Biometric Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to thank the National Science and Engineering Research Council (NSERC) (support via Discovery grant “Biometric intelligent interfaces”), Queen Elizabeth II Scholarship, and the Department of Electrical and Computer Engineering of the University of Calgary for their continuous support of this research.


  1. 1.
    Agrafioti, F., Gao, J., Hatzinakos, D.: Heart biometrics: theory, methods and applications. In: Yang, J., (ed.) Biometrics: Book 3, Intech, pp. 199–216 (2011)Google Scholar
  2. 2.
    Alivecor. Accessed Jan 2014
  3. 3.
    Bolle, R., Connell, J., Pankanti, S., et al.: Guide to Biometrics. Springer, New York (2004)CrossRefGoogle Scholar
  4. 4.
    Boulanov, O.R., Gavrilova, M.L., Poursaberi, A., et al.: Biometric-based intelligent agent systems. IADIS Int. Conf. Intell. Syst. Agents, Rome, Italy 24–26, 162–164 (2011)Google Scholar
  5. 5.
    Burdea, G.C., Coiffet, P.: Virtual Reality Technology, 2nd edn. Wiley, New York (2004)Google Scholar
  6. 6.
    Can, A., Steward, CV., Roysam, B., et al.: A feature-based, robust, hierarchical algorithm for registering pairs of images of the curved human retina. IEEE Trans. Anal. Mach. Intell. 24(3), 347–364 (2002)Google Scholar
  7. 7.
    Chen, K., Zhang, D.: Band selection for improvement of dorsal hand recognition. In: International Conference on Hand-Based Biometrics, pp. 1–4, 17–18 Nov 2011Google Scholar
  8. 8.
    Claes, P., Liberton, D.K., Daniels, K., et al.: Modeling 3D Facial Shape from DNA. PLOS Genet. 10(3), e1004224 (2014). doi: 10.1371/journal.pgen.1004224 Google Scholar
  9. 9.
    Cui, J., Wang, Y., Huang, J., et al.: An iris image synthesis method based on PCA and super-resolution. In: International Conference on Pattern Recognition, pp. 471–474, 23–26 Aug 2004Google Scholar
  10. 10.
    Du, Y., Lin, X.: Realistic mouth synthesis based on shape appearance dependence mapping. Pattern Recognit. Lett. 23(14), 1875–1885 (2002)zbMATHMathSciNetCrossRefGoogle Scholar
  11. 11.
    Duchaine, B., Nakayama, K.: Developmental prosopagnosia: a window to content-specific face processing. Curr. Opin. Neurobiol. 16(2), 166–173 (2006)CrossRefGoogle Scholar
  12. 12.
    Ekman, P., Rosenberg, E.L., (eds.): What the Face Reveals: Basic and Applied Studies of Spontaneous Expression Using the Facial Action Coding System (FACS). Oxford University Press, Oxford (1997)Google Scholar
  13. 13.
    Eveland, C.K., Socolinsky, D.A., Wolff, L.B.: Tracking human faces in infrared video. Image Vis. Comput. 21, 579–590 (2003)CrossRefGoogle Scholar
  14. 14.
    FaceGen. Accessed Nov 2013
  15. 15.
    Fanelli, G., Dantone, M., Gall, J., et al.: Real time head pose estimation with random regression forests. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 617–624, 20–25 June 2011Google Scholar
  16. 16.
    Foster, J.P., Nixon, M.S., Prüugel-Bennett, A.: Automatic gait recognition using area-based metrics pattern. Recogn. Lett. 24, 2489–2497 (2003)CrossRefGoogle Scholar
  17. 17.
    Franke, K., Ruiz-del-Solar, J.: Soft-biometrics: soft-computing technologies for biometric-applications. In: Pal, N.R., Sugeno, M. (eds.) Advances in Soft Computing AFSS, pp. 171–177. Springer, Berlin (2002)Google Scholar
  18. 18.
    Fu, Y., Guo, G., Huang, T.S.: Age synthesis and estimation via faces: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 32(11), 1955–1976 (2010)CrossRefGoogle Scholar
  19. 19.
    Fu, Y., Huang, T.S.: Human age estimation with regression on discriminative aging manifold. IEEE Trans. Multimedia 10(4), 578–584 (2008)Google Scholar
  20. 20.
    Fujimasa, I., Chinzei, T., Saito, I.: Converting far infrared image information to other physiological data. IEEE Eng. Med. Biol. Mag. 10(3), 71–76 (2000)CrossRefGoogle Scholar
  21. 21.
    GestureTek Health. Accessed March 2014
  22. 22.
    Google Glasses. Accessed May 2014
  23. 23.
    Guo, G., Fu, Y., Dyer, C., et al.: Image-based human age estimation by manifold learning and locally adjusted robust regression. IEEE Trans. Image Proces. 17(7), 1178–1188 (2008)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Jiang, H., Duerstock, B.S., Wachs, J.P.: A machine vision-based gestural interface for people with upper extremity physical impairments. IEEE Trans. Syst. Man Cybern. Syst. 44(5), 2168–2216 (2014)Google Scholar
  25. 25.
    Lai, K., Samoil, S., Yanushkevich, S.N.: Multi-spectral facial biometrics in access control. In: IEEE Symposium on Computational Intelligence in Biometrics and Identity Management, pp. 102–109, 9–12 Dec 2014Google Scholar
  26. 26.
    Lai, K., Samoil, S., Yanushkevich, S.: Application of biometric technologies in biomedical systems. In: International Conference on Digital Technologies, pp. 207–216, 9–11 July 2014Google Scholar
  27. 27.
    Lange, B., Chang, C., Suma, E., et al.: Development and evaluation of low cost game-based balance rehabilitation tool using Microsoft Kinect sensor. In: IEEE International Conference on Engineering in Medicine and Biology Society, pp. 1831–1834, 30–3 Sept 2011Google Scholar
  28. 28.
    Lanitis, A., Taylor, C.J., Cootes, T.F.: Toward automatic simulation of aging effects on face images. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 442–455 (2002)CrossRefGoogle Scholar
  29. 29.
    Leap Motion Incorporated. Introducing the 10 LEAP AXLR8R Teams. March 2014
  30. 30.
    Lefohn, A., Budge, B., Shirley, P., et al.: An ocularist’s approach to human iris synthesis. IEEE Mag. Comput. Graph. Appl 23(6), 70–75 (2003)CrossRefGoogle Scholar
  31. 31.
    Lo, B., Lee, H., Ing, M., et al.: Modeling of Facial Nerve Disorders. Undergraduate Project Report, Biometric Technologies Laboratory, University of Calgary (2006)Google Scholar
  32. 32.
    Maisto, M., Panella, M., Liparulo, L., et al.: An accurate algorithm for the identification of fingertips using an RGB-D camera. IEEE J. Emerg. Sel. Top. in Circuits Syst. 3(2), 272–283 (2013)Google Scholar
  33. 33.
    Mavridis, N., Petychakis, M., Tsamakos, A., et al.: FaceBots: steps towards enhanced long-term human-robot interaction by utilizing and publishing online social information. Paladyn 1(3), 169–178 (2010)Google Scholar
  34. 34.
    Mentis, H., Taylor, A.: Imaging the body: embodied vision in minimally invasive surgery. In: Proceedings of Human Factors in Computing Systems (2013). doi: 10.1145/2470654.2466197
  35. 35.
    Microsoft Kinect. Accessed Dec 2013
  36. 36.
    Microsoft Kinect for Windows. Accessed March 2014
  37. 37.
    Moriyama, T., Xiao, J., Kanade, T., et al.: Meticulously detailed eye model and its application to analysis of facial images. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 629–634, 10–13 Oct 2004Google Scholar
  38. 38.
    Nouse. Accessed March 2014
  39. 39.
    Nunamaker Jr, J.F., Derrick, D.C., Elkins, A.C., et al.: Embodied conversational agent based Kiosk for automated interviewing. J. Manage. Inf. Syst. 28(1), 17–48 (2011)CrossRefGoogle Scholar
  40. 40.
    Oliveira, C., Kaestner, C., Bortolozzi, F., et al.: Generation of signatures by deformation. In: Murshed, N.A., Bortolozzi, F. (eds.) Adv. Doc. Image Anal., pp. 283–298. Springer, Berlin (1997)CrossRefGoogle Scholar
  41. 41.
    Oliver, N., Pentland, A.P., Berard, F.: LAFTER: a real-time face and lips tracker with facial expression recognition. Pattern Recognit. 33(8), 1369–1382 (2000)CrossRefGoogle Scholar
  42. 42.
    Pantic, M., Rothkrantz, L.J.M.: Automatic analysis of facial expressions: the state-of-the-art. IEEE Trans. Pattern Anal. Mach. Intell. 22(12), 1424–1445 (2000)CrossRefGoogle Scholar
  43. 43.
    Patel, S., Park, H., Bonato, P., et al.: A review of wearable sensors and systems with application in rehabilitation. J. Neuro Eng. Rehabil. 9, 21 (2012). doi: 10.1186/1743-0003-9-21 CrossRefGoogle Scholar
  44. 44.
    Pavlidis, I., Levine, J.: Thermal image analysis for polygraph testing. IEEE Eng. Med. Biol. Mag. 21(6), 56–64 (2002)CrossRefGoogle Scholar
  45. 45.
    Rolls, E.T.: Toward automatic simulation of aging effects on face images. Behav. Process. 33(1–2), 113–138 (1994)CrossRefGoogle Scholar
  46. 46.
    Samoil, S., Lai, K., Yanushkevich, S.: Multispectral hand biometrics. In: International Conference on Emerging Security Technologies, pp. 24–29, 10–12 Sept 2014Google Scholar
  47. 47.
    Sanchez-Avila, C., Sanchez-Reillo, R.: Iris-based biometric recognition using dyadic wavelet transform. IEEE Aerosp. Electron. Syst. Mag. 17(10), 3–6 (2002)CrossRefGoogle Scholar
  48. 48.
  49. 49.
    Spree. Accessed March 2014
  50. 50.
    Sproat, R.W. (ed.): Multilingual Text-to-Speech Synthesis: The Bell Labs Approach. Kluwer Academics Publishers, Norwell (1997)Google Scholar
  51. 51.
    Sugimoto, Y., Yoshitomi, Y., Tomita, S.: A method for detecting transitions of emotional states using a thermal facial image based on a synthesis of facial expressions. Robot. Auton. Syst. 31(3), 147–160 (2000)CrossRefGoogle Scholar
  52. 52.
    Synthetic Fingerprint Generator. Accessed March 2014
  53. 53.
    TedCas Medical Systems.: TedCas integrates leap motion controller with medical imaging systems. Accessed March 2014
  54. 54.
    The Fingerprint Verification Competition FVC2004. Accessed March 2014
  55. 55.
    ThreeGear. Accessed June 2014
  56. 56.
    Tsumura, N., Ojima, N., Sato, K., et al.: Image-based skin color and texture analysis/synthesis by extracting hemoglobin and melanin information in the skin. ACM Trans. Graph. 22(3), 770–779 (2003)CrossRefGoogle Scholar
  57. 57.
    Wang, C., Liu, H., Liu, X.: Contact-free and pose-invariant hand-biometric-based personal identification system using RGB and depth data. J. Zhejiang Univ. Sci. C 15(7), 525–536 (2014)CrossRefGoogle Scholar
  58. 58.
    Wang, X., Tang, X.: Face photo-sketch synthesis and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(11), 1955–1967 (2009)CrossRefGoogle Scholar
  59. 59.
    Yamamoto, E., Nakamura, S., Shikano, K.: Lip movement synthesis from speech based on hidden markov models. Speech Commun. 26(1–2), 105–115 (1998)CrossRefGoogle Scholar
  60. 60.
    Yanushkevich, S.N., Stoica, A., Srihari, S.N., et al.: Simulation of biometric information: the new generation of biometric systems. In: International Workshop on Modeling and Simulation in Biometric Technology, pp. 87–98, 22–23 June 2004Google Scholar
  61. 61.
    Yanushkevich, S.N., Stoica, A., Shmerko, V.P., et al.: Biometric Inverse Problems. Taylor and Francis/CRC Press, Boca Raton (2005)zbMATHGoogle Scholar
  62. 62.
    Yanushkevich, S.N., Stoica, A., Shmerko, V.P.: Fundamentals of biometric-based training system design. In: Yanushkevich, S.N., Wang, P., Srihari, S., et al. (eds.) Image pattern recognition: synthesis and analysis in biometrics, Machine Perception and Artificial Intelligence, vol. 67, pp. 365–406. World ScientificGoogle Scholar
  63. 63.
    Zondervan, D.K., Reinkensmeyer, D.J.: Kinect-wheelchair interface controlled (KWIC) robotic trainer for powered mobility. In: International Conference of the IEEE Engineering in Medicine and Biology Society (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringBiometric Technologies Laboratory, University of CalgaryCalgaryCanada
  2. 2.École d’Ingénieurs et d’Architectes de FribourgHaute école spécialisée de Suisse OccidentaleFribourgSwitzerland

Personalised recommendations