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Emerging Trends in Security System Design Using the Concept of Social Behavioural Biometrics

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Information Fusion for Cyber-Security Analytics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 691))

Abstract

This chapter investigates how existing biometric research can be advanced by integrating it with the social behavioural information. Analytical discussions on how social behavioural biometrics can be extracted and applied in various security, and authentication applications will be presented. This chapter also provides some insights onto current and emerging research in the multimodal biometric domain, formulates open questions and investigates future directions. Answers to those questions will assist not only in establishment of the new methods in the biometric security domain but also provide insights into the future emerging topics in the big data analytics and social networking research.

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References

  1. Paul, P.P., Gavrilova, M.L., Alhajj, R.: Decision fusion for multimodal biometrics using social network analysis systems. IEEE Trans. Man. Cybern. Syst. 44(11), 522–1533 (2014)

    Google Scholar 

  2. Segalin, C., Perina, A., Cristani, M.: Personal aesthetics for soft biometrics: a generative multi-resolution approach. 16th International conference on multimodal interaction (ICMI '14), pp. 180–187 (2014)

    Google Scholar 

  3. Sultana, M., Paul, P.P., Gavrilova, M.: A concept of social behavioral biometrics: motivation, current developments, and future trends. International conference on Cyberworlds, pp. 271–278 (2014)

    Google Scholar 

  4. Yampolskiy, R., Gavrilova, M.: Artimetrics: biometrics for artificial entities. IEEE Robot Autom. Mag. 19(4), 48–58 (2012)

    Article  Google Scholar 

  5. Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Trans. Circuits Syst. Video Technol. 14(1), 420 (2004)

    Article  Google Scholar 

  6. Sultana, M., Paul, P.P., Gavrilova, M.: Mining social behavioral biometrics in Twitter. International conference on cyberworlds, pp. 293–299 (2014)

    Google Scholar 

  7. Sultana, M., Paul, P.P., Gavrilova, M.: Social behavioral biometrics: an emerging trend. Int. J. Pattern Recognit. Artif. Intell. 29(8), 1556013-1-20 (2015)

    Article  MathSciNet  Google Scholar 

  8. Gavrilova, M., Yampolskiy, R.: Applying biometric principles to avatar recognition. Trans. Comput. Sci. XII, 140–158 (2011)

    Article  Google Scholar 

  9. Drosou, A., Ioannidisa, D., Tzovarasa, D., Moustakasb, K., Petroua, M.: Activity related authentication using prehension biometrics. Pattern Recognit. 48(5), 1743–1759 (2015)

    Article  Google Scholar 

  10. Bazazian, S., Gavrilova, M.: A hybrid method for context-based gait recognition based on behavioral and social traits. Trans. Comput. Sci. LNCS 9030, 115–134 (2015)

    Google Scholar 

  11. Sultana, M., Paul, P.P., Gavrilova, M.: Identifying users from online interactions in Twitter. In: Gavrilova, M.L. (ed.) Transactions on Computational Science XXVI, pp. 111–124. Springer, Berlin (2016)

    Chapter  Google Scholar 

  12. Paul, P.P., Sultana, M., Matei, S.A., Gavrilova, M.L.: Editing behavior to recognize authors of crowdsourced content. IEEE international conference on systems, man, and cybernetics (SMC), pp. 1676–1681 (2015)

    Google Scholar 

  13. Haxby, J., Hoffman, E., Gobbini, I.: Human neural systems for face recognition and social communication. Biol Psychiatry 51(1), 59–67 (2002)

    Article  Google Scholar 

  14. Flickrdomain. https://www.flickr.com/. Accessed 19 Jan 2016

  15. Lovato, P., Bicego, M., Segalin, C., Perina, A., Sebe, N., Cristani, M.: Faved! Biometrics: tell me which image you like and I'll tell you who you are. IEEE Trans. Inf. Forensic Secur. 9(3), 364–374 (2014)

    Article  Google Scholar 

  16. Csurka, G., et al.: Visual categorization with bags of keypoints. Workshop on statistical learning in computer vision, ECCV 1, pp 1–22 (2004)

    Google Scholar 

  17. Pinterest domain. https://www.pinterest.com/. Accessed: 19 Jan 2016

  18. Leder, H., Belke, B., Oeberst, A., Augustin, D.: A model of aesthetic appreciation and aesthetic judgments. Br. J. Psychol. 95(4), 489–508 (2004)

    Article  Google Scholar 

  19. Aydin, T., Smolic, A., Gross, M.: Automated aesthetic analysis of photographic images. IEEE Trans. Vis. Comput. Graph 21(1), 31–42 (2015)

    Article  Google Scholar 

  20. Jiang, W., Loui, A., Cerosaletti, C. (2010) Automatic aesthetic value assessment in photographic images. 2010 I.E. international conference on multimedia and expo (ICME), pp. 920–925 (2010).

    Google Scholar 

  21. Marchesotti, L., Perronnin, F., Larlus, D., Csurka, G.: Assessing the aesthetic quality of photographs using generic image descriptors. IEEE international conference on computer vision (ICCV), pp.1784–1791 (2011)

    Google Scholar 

  22. Xiaohui, W., Jia, J., Yin, J., Cai, L.: Interpretable aesthetic features for affective image classification. 20th IEEE international conference on image processing (ICIP), pp. 3230–3234 (2013).

    Google Scholar 

  23. Isola, P., Xiao, J., Parikh, D., Torralba, A., Oliva, A.: What makes a photograph memorable? IEEE Trans. Pattern Anal. Mach. Intell. 36(7), 1469–1482 (2014)

    Article  Google Scholar 

  24. Khosla, A., Sarma, A.D., Hamid, R.: What makes an image popular? 23rd international conference on World wide web (WWW '14), pp. 867–876 (2014)

    Google Scholar 

  25. Gavrilova, M., Monwar, M.: Multimodal Biometrics and Intelligent Image Processing for Security Systems. IGI book, Hershey, PA (2013)

    Book  Google Scholar 

  26. Vinciarelli, A., Pantic, M., Heylen, D., Pelachaud, C., Poggi, I., D'Errico, F., Schroeder, M.: Bridging the gap between social animal and unsocial machine: a survey of social signal processing. IEEE Trans. Affect Comput. 3(1), 69–87 (2012)

    Article  Google Scholar 

  27. Chew, S., Lucey, P., Lucey, S., Saragih, J., Cohn, J., Matthews, I., Sridharan, S.: In the pursuit of effective affective computing: the relationship between features and registration. IEEE Trans. Syst. Man. Cybern. B 42(4), 1006–1016 (2012)

    Article  Google Scholar 

  28. Eleftheriadis, S., Rudovic, O., Pantic, M.: Discriminative shared Gaussian processes for multiview and view-invariant facial expression recognition. IEEE Trans. Image Process 24(1), 189–204 (2015)

    Article  MathSciNet  Google Scholar 

  29. Li, Y., Wang, S., Zhao, Y., Ji, Q.: Simultaneous Facial Feature Tracking and Facial Expression Recognition. IEEE Trans. Image Process 22(7), 2559–2573 (2013)

    Article  Google Scholar 

  30. Littlewort, G., Whitehill, J., Wu, T., Fasel, I., Frank, M., Movellan, J., Bartlett, M.: The computer expression recognition toolbox (CERT). IEEE International conference of automatic face and gesture recognition workshops, pp. 298–305 (2011)

    Google Scholar 

  31. Lucey, S., Matthews, I., Hu, C., Ambadar, Z., Cohn, J.: AAM derived face representations for robust facial action recognition. IEEE International conference of automatic face and gesture recognition, pp. 155–160 (2006)

    Google Scholar 

  32. Tian, Y.L., Kanade, T., Cohn, J.F.: Recognizing action units for facial expression analysis. IEEE Trans. Pattern Anal. Mach. Intell. 23(2), 97–115 (2001)

    Article  Google Scholar 

  33. Wang, S., Liu, Z., Lv, S., Lv, Y., Wu, G., Peng, P., Chen, F., Wang, X.: A natural visible and infrared facial expression data-base for expression recognition and emotion inference. IEEE Trans. Multimedia 12(7), 682–691 (2010)

    Article  Google Scholar 

  34. Sariyanidi, S., Gunes, H., Cavallaro, A.: Automatic analysis of facial affect: a survey of registration, representation, and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(6), 1113–1133 (2015)

    Article  Google Scholar 

  35. Ekman, P., Friesen, W.: Facial Action Coding System: A Technique for the Measurement of Facial Movement. Consulting Psy-chologists Press, Palo Alto, CA (1978)

    Google Scholar 

  36. Boucenna, S., Gaussier, P., Hafemeister, L.: Development of first social referencing skills: emotional interaction as a way to regulate robot behavior. IEEE Trans. Autonom Mental Dev. 6(1), 42–55 (2014)

    Article  Google Scholar 

  37. Barakova, E., Gorbunov, R., Rauterberg, M.: Automatic interpretation of affective facial expressions in the context of interpersonal interaction. IEEE Trans. Hum. Mach. Syst. 45(4), 409–418 (2015)

    Article  Google Scholar 

  38. Bae, M., Park, I.: Content-based 3d model retrieval using a single depth image from a low-cost 3d camera. Visual Comput. 29, 555–564 (2013)

    Article  Google Scholar 

  39. Barth, J., Klucken, J., Kugler, P., Kammerer, T., Steidl, R., Winkler, J., Hornegger, J., Eskofier, B.: Biometric and mobile gait analysis for early diagnosis and therapy monitoring in Parkinson’s disease. Annual international conference of the IEEE engineering in medicine and biology society, EMBC, pp. 868–871 (2011)

    Google Scholar 

  40. Zhang, Y., Zheng, J., Magnenat-Thalmann, N.: Example-guided anthropometric human body modeling. Visual Comput. CGI 2014, 1–17 (2014)

    Google Scholar 

  41. Zhou, L., Zhiwu, L., Leung, H., Shang, L.: Spatial temporal pyramid matching using temporal sparse representation for human motion retrieval. Visual Comput. 30, 845–854 (2014)

    Article  Google Scholar 

  42. Drosou, A.: Activity related biometrics for person authentication. PhD thesis, Imperial College London (2014)

    Google Scholar 

  43. Ferro, M., Pioggia, G., Tognetti, A., Carbonaro, N., Rossi, D.D.: A sensing seat for human authentication. IEEE Trans. Inf. Forensic Secur. 4(3), 451–459 (2009)

    Article  Google Scholar 

  44. Stone, E., Skubic, M.: Evaluation of an inexpensive depth camera for passive in-home fall risk assessment. International pervasive computing technologies for healthcare conference, pp. 71–77 (2011)

    Google Scholar 

  45. Chang, Y., Chen, S., Huang, J.: A kinect-based system for physical rehabilitation: a pilot study for young adults with motor disabilities. Res. Dev. Disabil. 32(6), 2566–2570 (2011)

    Article  Google Scholar 

  46. Popa, M., Koc, A., Rothkrantz, L., Shan, C., Wiggers, P.: Kinect sensing of shopping related actions. Commun. Comput. Inf. Sci. 277, 91–100 (2012)

    Article  Google Scholar 

  47. Ball, A., Rye, D., Ramos, F., Velonaki, M.: Unsupervised clustering of people from ‘skeleton’ data. ACM/IEEE international conference on human robot interaction, pp. 225–226 (2012)

    Google Scholar 

  48. Preis, J., Kessel, M., Linnhoff-Popien, C., Werner, M.: Gait recognition with kinect. Workshop on kinect in pervasive computing (2012)

    Google Scholar 

  49. Ahmed, F., Paul, P., Gavrilova, M.: Dtw-based kernel and rank level fusion for 3d gait recognition using Kinect. Visual Comput. 31(6-8), 915–924 (2015)

    Article  Google Scholar 

  50. Ahmed, F., Gavrilova, M.: Biometric-based user authentication and activity level detection in a collaborative environment. In: Matei, S.A., et al. (eds.) Transparency in Social Media, pp. 166–179. Springer, New York, NY (2015)

    Google Scholar 

  51. Sanei, S., Chambers, J.: EEG Signal Processing. John Wiley & Sons Ltd, England (2007)

    Book  Google Scholar 

  52. Webster, J.G.: Medical Instrumentation Application and Design. Medical Imaging and Instrumentation Laboratory, Stanford, CA (2009)

    Google Scholar 

  53. Types of brain waves online article http://mentalhealthdaily.com/2014/04/15/5-types-of-brain-waves-frequencies-gamma-beta-alpha-theta-delta/. Accessed 17 Jan 2016

    Google Scholar 

  54. Nguyen, P., Tran, D., Huang, X., Sharma, D.: A proposed feature extraction method for EEG based person identification. In: International conference artificial intelligence (ICAI) (2012)

    Google Scholar 

  55. Smit, D., Posthuma, D., Boomsma, D.I., Geus, E.J.C.: Heritability of background EEG across the power spectrum. Psychophysiology 42(6), 691–697 (2005)

    Article  Google Scholar 

  56. Ruiz Blondet, M., Laszlo, S., Jin, Z.: Assessment of permanence of non-volitional EEG brainwaves as a biometric. International conference on identity, security and behavior analysis (ISBA) (2015)

    Google Scholar 

  57. Whitehill, J., Serpell, Z., Lin, Y.C., Foster, A., Movellan, J.R.: The faces of engagement: automatic recognition of student engagement from facial expressions. IEEE Trans. Affect Comput. 5(1), 86–98 (2014)

    Article  Google Scholar 

  58. Cohn, J.F.: Advances in behavioral science using automated facial image analysis and synthesis [social sciences]. Signal Proc. Mag. 27(6), 128–133 (2010)

    Google Scholar 

  59. Poursaberi, A., Vana, J., Mráček, S., Dvora, R., Yanushkevich, S.N., Dra-hansky, M., Shmerko, V.P., Gavrilova, M.L.: Facial biometrics for situational awareness systems. IET Biometrics 2(2), 35–47 (2013)

    Article  Google Scholar 

  60. McDuff, D., El Kaliouby, R., Cohn, J.F., Picard, R.W.: Predicting Ad liking and purchase intent: large-scale analysis of facial responses to Ads. IEEE Trans. Affect Comput. 6(3), 223–235 (2015)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. University of Calgary, Calgary, AB, Canada

    M. L. Gavrilova, F. Ahmed, S. Azam, P. P. Paul, W. Rahman, M. Sultana & F. T. Zohra

Authors
  1. M. L. Gavrilova
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  2. F. Ahmed
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  3. S. Azam
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  4. P. P. Paul
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  5. W. Rahman
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  6. M. Sultana
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  7. F. T. Zohra
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Corresponding author

Correspondence to M. L. Gavrilova .

Editor information

Editors and Affiliations

  1. Department of Computing and Cyber Security, University of Texas A&M, San Antonio, Texas, USA

    Izzat M Alsmadi

  2. Department of Information Systems, University of Maryland Baltimore County (UMBC), Baltimore, Maryland, USA

    George Karabatis

  3. Department of Computer Information Systems, Yarmouk University, Irbid, Jordan

    Ahmed Aleroud

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Gavrilova, M.L. et al. (2017). Emerging Trends in Security System Design Using the Concept of Social Behavioural Biometrics. In: Alsmadi, I., Karabatis, G., Aleroud, A. (eds) Information Fusion for Cyber-Security Analytics. Studies in Computational Intelligence, vol 691. Springer, Cham. https://doi.org/10.1007/978-3-319-44257-0_10

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  • DOI: https://doi.org/10.1007/978-3-319-44257-0_10

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  • Print ISBN: 978-3-319-44256-3

  • Online ISBN: 978-3-319-44257-0

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