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Survey of Biometric Techniques for Automotive Applications

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Information Technology - New Generations

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 738))

Abstract

Although significant research has been dedicated to developing biometric solutions for motorized vehicles, there are currently no survey works charting the progress in this field. This paper discusses a selection of biometrics research focusing on improving vehicle safety and protecting vehicles against theft. Specifically, we discuss research that focuses on detecting a driver’s impaired ability to control the vehicle due to drowsiness, intoxication, or a medical emergency; developing techniques for identifying and preventing intrusions into the vehicle; and discovering driver distractions from within and without the vehicle. We also comment on the potential effectiveness, user-friendliness, privacy, security, and other aspects of the proposed approaches and identify directions for future research.

We supplement this paper with a comprehensive list of other works in the field, which is accessible from Gofman and Villa (Extended database of biometrics research for automotive applications, 2017. http://www.fullerton.edu/cybersecurity/research/Extended-Database-of-Biometrics-Research-for-Automotive-Applications.php).

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References

  1. 3pixelart.com. http://www.apexengineeringproject.com/display-product.php?id=AP106

  2. J. Adell, P. Jodrá, Exact kolmogorov and total variation distances between some familiar discrete distributions. J. Inequal. Appl. 2006(1), 64307 (2006)

    Google Scholar 

  3. F. Althoff, R. Lindl, L. Walchshausl, S. Hoch, Robust multimodal hand-and head gesture recognition for controlling automotive infotainment systems. VDI BERICHTE 1919, 187 (2005)

    Google Scholar 

  4. Artificial intelligence helps to keep tired drivers awake (2017). http://www.digitaljournal.com/tech-and-science/technology/artificial-intelligence-helps-to-keep-tired-drivers-awake/article/499369

  5. Automotive. http://www.optalert.com/automotive

  6. P.N. Belhumeur, J.P. Hespanha, D.J. Kriegman, Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)

    Google Scholar 

  7. S. Ben-Yacoub, B. Fasel, Fast Multi-Scale Face Detection (IDIAP, Martigny, 1998)

    Google Scholar 

  8. M. Billinghurst, B. Buxton, Gesture based interaction, in Haptic Input, 24 (2011)

    Google Scholar 

  9. K.W. Bowyer, K. Chang, P. Flynn, A survey of approaches and challenges in 3D and multi-modal 3D+ 2D face recognition. Comput. Vis. Image Underst. 101(1), 1–15 (2006)

    Google Scholar 

  10. CBCL face recognition database. http://cbcl.mit.edu/software-datasets/heisele/facerecognition-database.html

  11. R. Chen, M. She, J. Wang, X. Sun, L. Kong, Driver verification based on handgrip recognition on steering wheel, in 2011 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (IEEE, New York, 2011), pp. 1645–1651

    Google Scholar 

  12. X. Chen, P.J. Flynn, K.W. Bowyer, PCA-based face recognition in infrared imagery: baseline and comparative studies, in IEEE International Workshop on Analysis and Modeling of Faces and Gestures, AMFG 2003 (IEEE, New York, 2003), pp. 127–134

    Google Scholar 

  13. O. Dehzangi, C. Williams, Towards multi-modal wearable driver monitoring: impact of road condition on driver distraction, in 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) (IEEE, New York, 2015), pp. 1–6

    Google Scholar 

  14. C. Endres, T. Schwartz, C.A. Müller, Geremin: 2D microgestures for drivers based on electric field sensing, in Proceedings of the 16th International Conference on Intelligent User Interfaces (ACM, New York, 2011), pp. 327–330

    Google Scholar 

  15. C.O. Folorunso, L.A. Akinyemi, A.A. Ajasa, K. Oladipupo, Design and development of fingerprint based car starting system presentation made at 16th International Conference on Electronic Packaging Technology (ICEPT 2015), Exhibition on Power and Telecommunications, http://nieee.org.ng/portfolio/papers/view/Design_and_ Development_of_Fingerprint_based_Car_Starting_System_(Folorunsho_ C_et_al.pdf

  16. Y. Freund, R.E. Schapire, A desicion-theoretic generalization of on-line learning and an application to boosting, in European Conference on Computational Learning Theory (Springer, Berlin, 1995), pp. 23–37

    Google Scholar 

  17. Future jaguar cars may recognize approaching drivers (2016). http://findbiometrics.com/jaguar-cars-face-biometrics-311247/

  18. Gestigon, The future of mobility is not about cars. http://www.gestigon.com/automotive-industry/

  19. M. Gofman, M. Villa, Extended database of biometrics research for automotive applications. (2017) http://www.fullerton.edu/cybersecurity/research/Extended-Database-of-Biometrics-Research-for-Automotive-Applications.php

  20. M. Gutmann, P. Grausberg, K. Kyamakya, Detecting human driver’s physiological stress and emotions using sophisticated one-person cockpit vehicle simulator, in Information Technologies in Innovation Business Conference (ITIB) (IEEE, New York, 2015), pp. 15–18

    Google Scholar 

  21. K. Igarashi, C. Miyajima, K. Itou, K. Takeda, F. Itakura, H. Abut, Biometric identification using driving behavioral signals, in 2004 IEEE International Conference on Multimedia and Expo, 2004. ICME’04, vol. 1 (IEEE, New York, 2004), pp. 65–68

    Google Scholar 

  22. K.A. Ishak, S.A. Samad, A. Hussain, A face detection and recognition system for intelligent vehicles. Inf. Technol. J. 5(3), 507–515 (2006)

    Google Scholar 

  23. R. Ivers, T. Senserrick, S. Boufous, M. Stevenson, H.-Y. Chen, M. Woodward, R. Norton, Novice drivers’ risky driving behavior, risk perception, and crash risk findings from the drive study (2009). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2724457/

  24. Jaguar files patent for vehicle access system with facial recognition and gait analysis (2016). http://www.biometricupdate.com/201611/jaguar-files-patent-for-vehicle-access-system-with-facial-recognition-and-gait-analysis

  25. J.B. Jadav, K.H. Wandra, R. Dabhi, Innovative automobile security system using various security modules, International Journal of Scientific Progress and Research 8(1), 24–27

    Google Scholar 

  26. J. Kang, D.V. Anderson, M.H. Hayes, Face recognition in vehicles with near infrared frame differencing, in IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE) (IEEE, New York, 2015), pp. 358–363

    Google Scholar 

  27. N. Kiruthiga, S. Thangasamy, et al., Real time biometrics based vehicle security system with GPS and GSM technology. Proc. Comput. Sci. 47, 471–479 (2015)

    Google Scholar 

  28. S.K. Kopparapu, A robust speech biometric system for vehicle access, in 2009 IEEE International Conference on Vehicular Electronics and Safety (ICVES) (IEEE, New York, 2009), pp. 174–177

    Google Scholar 

  29. H.B. Lee, J.M. Choi, J.S. Kim, Y.S. Kim, H.J. Baek, M.S. Ryu, R.H. Sohn, K.S. Park, Nonintrusive biosignal measurement system in a vehicle, in 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2007 (IEEE, New York, 2007), pp. 2303–2306

    Google Scholar 

  30. J. Lee, Omron integrating facial recognition into autonomous driving system (2016). http://www.biometricupdate.com/201610/omron-integrating-facial-recognition-into-autonomous-driving-system

  31. A. Leonardis, H. Bischof, Dealing with occlusions in the eigenspace approach, in 1996 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1996. Proceedings CVPR’96 (IEEE, New York, 1996), pp. 453–458

    Google Scholar 

  32. libfprint API reference. http://www.reactivated.net/fprint/api/

  33. Z. Liu, A new embedded car theft detection system, in Second International Conference Onembedded Software and Systems, 2005 (IEEE, New York, 2005), 6 pp.

    Google Scholar 

  34. Z. Liu, G. He, Research on vehicle anti-theft and alarm system using facing recognition, in International Conference on Neural Networks and Brain, 2005. ICNN&B’05, vol. 2 (IEEE, New York, 2005), pp. 925–929

    Google Scholar 

  35. C. Lupu, V. Lupu, Multimodal biometrics for access control in an intelligent car, in International Symposium on Computational Intelligence and Intelligent Informatics, 2007. ISCIII’07 (IEEE, New York, 2007), pp. 261–267

    Google Scholar 

  36. I.D. Markwood, Y. Liu, Vehicle self-surveillance: sensor-enabled automatic driver recognition, in Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security (ACM, New York, 2016), pp. 425–436

    Google Scholar 

  37. S. Mayhew, Ford granted patent for keyless biometric system for vehicles (2015). http://www.biometricupdate.com/201502/ford-granted-patent-for-keyless-biometric-system-for-vehicles

  38. A. Meschtscherjakov, H. Scharfetter, S.P. Kernjak, N.M. Kratzer, J. Stadon, Adaptive digital sunshade: blocking the sun from blinding the driver, in Adjunct Proceedings of the 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (ACM, New York, 2015), pp. 78–83

    Google Scholar 

  39. Mobile ID World, Keeping drivers safe with pupil biometrics (2016). https://findbiometrics.com/keeping-drivers-safe-with-pupil-biometrics-301058/

  40. National Highway Traffic Safety Administration et al., Early estimate of motor vehicle traffic fatalities for the first 9 months of 2016 (2017). https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812358

  41. NHTSA, Contribution of medical conditions to passenger vehicle crashes (2009). https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/811219

  42. NHTSA, Drunk driving (2017). https://www.nhtsa.gov/risky-driving/drunk-driving

  43. D. Nosowitz, A car seat that authenticates the driver with butt recognition (2011). https://www.popsci.com/cars/article/2011-12/car-seat-recognizes-your-butt-security-and-fun

  44. O. Omeni, A.C. Wai Wong, A.J. Burdett, C. Toumazou, Energy efficient medium access protocol for wireless medical body area sensor networks. IEEE Trans. Biomed. Circuits Syst. 2(4), 251–259 (2008)

    Article  Google Scholar 

  45. M. Oravec, J. Pavlovicova, Face recognition methods based on principal component analysis and feedforward neural networks, in Proceedings of 2004 IEEE International Joint Conference on Neural Networks, vol. 1 (IEEE, New York, 2004), pp. 437–441

    Google Scholar 

  46. S. Padmapriya, E.A. KalaJames, Real time smart car lock security system using face detection and recognition, in International Conference on Computer Communication and Informatics (ICCCI) (IEEE, New York, 2012), pp. 1–6

    Google Scholar 

  47. A. Pentland, B. Moghaddam, T. Starner, et al., View-based and modular eigenspaces for face recognition, in CVPR, vol. 94 (1994), pp. 84–91

    Google Scholar 

  48. R. Rathore, C. Gau, Integrating biometric sensors into automotive internet of things, in International Conference on Cloud Computing and Internet of Things (CCIOT) (2014), pp. 178–181

    Google Scholar 

  49. A. Reyes-Muñoz, M.C. Domingo, M.A. López-Trinidad, J.L. Delgado, Integration of body sensor networks and vehicular ad-hoc networks for traffic safety. Sensors 16(1), 107 (2016)

    Google Scholar 

  50. H.A. Rowley, S. Baluja, T. Kanade, Human face detection in visual scenes, in Advances in Neural Information Processing Systems (1996), pp. 875–881

    Google Scholar 

  51. H. Samet, The quadtree and related hierarchical data structures. ACM Comput. Surv.(CSUR) 16(2), 187–260 (1984)

    Google Scholar 

  52. Sense holdings acquires exclusive sales, marketing and purchase rights for biometric patent to secure vehicles. http://www.theautochannel.com/news/2004/03/18/185331.html

  53. T. Sim, S. Baker, M. Bsat, The CMU pose, illumination, and expression (PIE) database, in Proceedings Fifth IEEE International Conference on Automatic Face and Gesture Recognition (IEEE, New York, 2002), pp. 53–58

    Google Scholar 

  54. N. Sushmitha, B. Supriya, R. Prajeeshan, Bio-metric automobile security, International Journal of Scientific Engineering and Technology Research. 4(19), 3550–3554 (2015)

    Google Scholar 

  55. G.A. ten Holt, M.J.T. Reinders, E.A. Hendriks, Multi-dimensional dynamic time warping for gesture recognition, in Thirteenth Annual Conference of the Advanced School for Computing and Imaging, vol. 300 (2007)

    Google Scholar 

  56. The power to stop drunk driving is now in the palm of your hand. http://sobersteering.com/how-it-works/

  57. H.C. Tijms, Stochastic Models: An Algorithmic Approach, vol. 303 (Wiley, London, 1994)

    MATH  Google Scholar 

  58. M. Turk, A. Pentland, Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)

    Article  Google Scholar 

  59. Vigo the science. https://www.wearvigo.com/science

  60. L. Yao, C. Lin, J. Deng, F. Deng, J. Miao, K. Yim, G. Wu, Biometrics-based data link layer anonymous authentication in vanets, in 2013 Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS) (IEEE, New York, 2013), pp. 182–187

    Google Scholar 

  61. I.J. Faulks,. M. Regan, M. Stevenson, J. Brown, A. Porter, J.D. Irwin (Eds.). Distracted driving. Sydney, NSW: Australasian College of Road Safety, pp. 379–405

    Google Scholar 

  62. W. Yuan, Y. Tang, The driver authentication device based on the characteristics of palmprint and palm vein, in 2011 International Conference on Hand-Based Biometrics (ICHB) (IEEE, New York, 2011), pp. 1–5

    Google Scholar 

  63. W. Zhao, R. Chellappa, A. Krishnaswamy, Discriminant analysis of principal components for face recognition, in Proceedings of third IEEE International Conference on Automatic Face and Gesture Recognition, 1998 (IEEE, New York, 1998), pp. 336–341

    Google Scholar 

  64. Z. Zhu, F. Chen, Fingerprint recognition-based access controlling system for automobiles, in 2011 4th International Congress on Image and Signal Processing (CISP), vol. 4 (IEEE, New York, 2011), pp. 1899–1902

    Book  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, California State University, Fullerton, CA, USA

    Maria Villa & Mikhail Gofman

  2. Department of Information Systems and Decision Sciences, California State University, Fullerton, CA, USA

    Sinjini Mitra

Authors
  1. Maria Villa
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  2. Mikhail Gofman
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  3. Sinjini Mitra
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Corresponding author

Correspondence to Mikhail Gofman .

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Editors and Affiliations

  1. Department of Electrical & Computer Engineering, University of Nevada, Las Vegas, Las Vegas, Nevada, USA

    Shahram Latifi

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Villa, M., Gofman, M., Mitra, S. (2018). Survey of Biometric Techniques for Automotive Applications. In: Latifi, S. (eds) Information Technology - New Generations. Advances in Intelligent Systems and Computing, vol 738. Springer, Cham. https://doi.org/10.1007/978-3-319-77028-4_62

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  • DOI: https://doi.org/10.1007/978-3-319-77028-4_62

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