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Biometrics for Biomedical Applications

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Applications of Computational Intelligence in Biomedical Technology

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

Abstract

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.

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References

  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. Alivecor. http://www.alivecor.com/home. Accessed Jan 2014

  3. Bolle, R., Connell, J., Pankanti, S., et al.: Guide to Biometrics. Springer, New York (2004)

    Book  Google Scholar 

  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. Burdea, G.C., Coiffet, P.: Virtual Reality Technology, 2nd edn. Wiley, New York (2004)

    Google Scholar 

  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. 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 2011

    Google Scholar 

  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. 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 2004

    Google Scholar 

  10. Du, Y., Lin, X.: Realistic mouth synthesis based on shape appearance dependence mapping. Pattern Recognit. Lett. 23(14), 1875–1885 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  11. Duchaine, B., Nakayama, K.: Developmental prosopagnosia: a window to content-specific face processing. Curr. Opin. Neurobiol. 16(2), 166–173 (2006)

    Article  Google Scholar 

  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. Eveland, C.K., Socolinsky, D.A., Wolff, L.B.: Tracking human faces in infrared video. Image Vis. Comput. 21, 579–590 (2003)

    Article  Google Scholar 

  14. FaceGen. http://www.facegen.com/. Accessed Nov 2013

  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 2011

    Google Scholar 

  16. Foster, J.P., Nixon, M.S., Prüugel-Bennett, A.: Automatic gait recognition using area-based metrics pattern. Recogn. Lett. 24, 2489–2497 (2003)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  21. GestureTek Health. http://www.gesturetekhealth.com. Accessed March 2014

  22. Google Glasses. https://developers.google.com/glass. Accessed May 2014

  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)

    Article  MathSciNet  Google Scholar 

  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. 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 2014

    Google Scholar 

  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 2014

    Google Scholar 

  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 2011

    Google Scholar 

  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)

    Article  Google Scholar 

  29. Leap Motion Incorporated. Introducing the 10 LEAP AXLR8R Teams. https://developer.leapmotion.com/blog/introducing-the-10-leap-axlr8r-teams/Accessed March 2014

  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)

    Article  Google Scholar 

  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. 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. 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. 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. Microsoft Kinect. http://www.microsoft.com/en-us/kinectforwindows/. Accessed Dec 2013

  36. Microsoft Kinect for Windows. http://www.microsoft.com/en-us/kinectforwindows/default.aspx. Accessed March 2014

  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 2004

    Google Scholar 

  38. Nouse. http://www.nouse.ca/en/. Accessed March 2014

  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)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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

    Article  Google Scholar 

  44. Pavlidis, I., Levine, J.: Thermal image analysis for polygraph testing. IEEE Eng. Med. Biol. Mag. 21(6), 56–64 (2002)

    Article  Google Scholar 

  45. Rolls, E.T.: Toward automatic simulation of aging effects on face images. Behav. Process. 33(1–2), 113–138 (1994)

    Article  Google Scholar 

  46. Samoil, S., Lai, K., Yanushkevich, S.: Multispectral hand biometrics. In: International Conference on Emerging Security Technologies, pp. 24–29, 10–12 Sept 2014

    Google Scholar 

  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)

    Article  Google Scholar 

  48. Scopis GmbH. Touchless control of a surgical navigation system. http://www.scopis.com/en/news/news/details/archive/2013/may/03/article/beruehrungslose-steuerung-eines-klinischen-navigationssystems/. Accessed July 2013

  49. Spree. http://spreesports.com. Accessed March 2014

  50. Sproat, R.W. (ed.): Multilingual Text-to-Speech Synthesis: The Bell Labs Approach. Kluwer Academics Publishers, Norwell (1997)

    Google Scholar 

  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)

    Article  Google Scholar 

  52. Synthetic Fingerprint Generator. http://bias.csr.unibo.it/research/biolab/sfinge.html. Accessed March 2014

  53. TedCas Medical Systems.: TedCas integrates leap motion controller with medical imaging systems. http://www.tedcas.com/en/node/1562. Accessed March 2014

  54. The Fingerprint Verification Competition FVC2004. http://bias.csr.unibo.it/fvc2004/databases.asp. Accessed March 2014

  55. ThreeGear. http://www.threegear.com/. Accessed June 2014

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  58. Wang, X., Tang, X.: Face photo-sketch synthesis and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(11), 1955–1967 (2009)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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 2004

    Google Scholar 

  61. Yanushkevich, S.N., Stoica, A., Shmerko, V.P., et al.: Biometric Inverse Problems. Taylor and Francis/CRC Press, Boca Raton (2005)

    MATH  Google Scholar 

  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 Scientific

    Google Scholar 

  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 

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Acknowledgments

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.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Biometric Technologies Laboratory, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada

    Kenneth Lai, Steven Samoil & Svetlana N. Yanushkevich

  2. École d’Ingénieurs et d’Architectes de Fribourg, Haute école spécialisée de Suisse Occidentale, Bd de Pérolles 80, Box 32, 1705, Fribourg, Switzerland

    Gaetan Collaud

Authors
  1. Kenneth Lai
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  2. Steven Samoil
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  3. Svetlana N. Yanushkevich
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  4. Gaetan Collaud
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Corresponding author

Correspondence to Kenneth Lai .

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Editors and Affiliations

  1. Katedra aplikované matematiky, VŠB - Technical University of Ostrava, Ostrava-Poruba, Czech Republic

    Radim Bris

  2. Pavol Jozef Safarik University, Kosice, Slovakia

    Jaroslav Majernik

  3. University of Information Technology and Management in Rzeszow, Rzeszow, Poland

    Krzysztof Pancerz

  4. Faculty of Management Science and Informatics, University of Zilina, Zilina, Slovakia

    Elena Zaitseva

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Lai, K., Samoil, S., Yanushkevich, S.N., Collaud, G. (2016). Biometrics for Biomedical Applications. In: Bris, R., Majernik, J., Pancerz, K., Zaitseva, E. (eds) Applications of Computational Intelligence in Biomedical Technology. Studies in Computational Intelligence, vol 606. Springer, Cham. https://doi.org/10.1007/978-3-319-19147-8_8

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  • DOI: https://doi.org/10.1007/978-3-319-19147-8_8

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