Biometric tendency recognition and classification system: an artistic approach

ABSTRACT

The Biometric Tendency Recognition and Classification System is a software application that continuously measures a participant's physiological responses to a given image and runs a statistical classification algorithm on the measured data that then classifies the participant into one of four categories: passive, aggressive, loyal and subversive. The system is part of an interactive art project that explores issues of authority, privacy and security in relation to biometric technologies. In this paper, we demonstrate the development of this system that exemplifies the use of biometrics within the context of art.

REFERENCES

• 1: Thie, L. and Fried, E. 2004. Acclair, http://www.acclair.co.uk/, accessed May 10, 2008
• 2: Hutzler, G., Gortais, B., and Drogoul, A. 2000. The Garden of Chance: A Visual Ecosystem, Leonardo, Vol. 33, No. 2 (Apr. 2000), 101--106.
• 3: Gromala, D. 2003. Biomorphic Typography, TypeO Exhibition, FACT (Foundation for Art & Creative Technology), Liverpool, England.
• 4: Gilchrist, B. and Bradley, J. 1996. Divided by Resistance. http://www.artemergent.org.uk/dbr/dbr.html, acessed June 29, 2008.
• 5: Rosenboom, D. 1976. Biofeedback and the Arts. Vancouver: A.R.C. Publications.
• 6: Miranda, E. R., 2006. Biosignal Interfaces. In New Digital Musical Instruments: Control And Interaction Beyond the Keyboard. A-R Editions, Middleton, WI, 173--217.
• 7: Picard, R. W. 1995. Affective Computing, M.I.T Media Laboratory Perceptual Computing Section Technical Report No. 321.
• 8: Luther Thie , Eyal Fried, Acclairism: questioning biometric technology through an airport security clearance system, CHI '05 extended abstracts on Human factors in computing systems, April 02-07, 2005, Portland, OR, USA [doi>10.1145/1056808.1056856]
• 9: Thought technology Ltd. http://www.thoughttechnology.com/, accessed May 6, 2008
• 10: Ebersole, J. S. 1991. EEG dipole modeling in complex partial epilepsy. Brain Topography, Volume 4, Number 2 (Dec. 1991), 113--123.
• 11: Drake, M. E., Pakalnis, A., Andrews, J. M., and Bogner, J. E. 1990. Nocturnal Sleep Recording with Cassette EEG in Chronic Headaches. Headache: The Journal of Head and Face Pain, Volume 30, Issue 9 (Sep. 1990), 600--603.
• 12: Luck, S. J. 2005. An Introduction to the Event-Related Potential Technique. The MIT Press, Cambridge, MA.
• 13: Sutton, S., Braren, M., Zubin, J., and John, E. R. 1965. Evoked-Potential Correlates of Stimulus Uncertainty. Science, 150 (3700), 1187--1188.
• 14: Farwell, L. A. and Donchin, E. 1989. Detection of Guilty Knowledge with ERPs. Psychophysiology, 26:58. (Abstract of an address presented at the Twenty-Eighth Annual Meeting of the Society for Psychophysiological Research, Oct. 1989).
• 15: Farwell, L. A. 1993. Brain MERMERs: Detection of FBI Agents and Crime-Relevant Information with the Farwell MERA System. Proceedings of the International Security Systems Symposium, Washington, D.C.
• 16: Makeig, T. H., counsel to Dr. Lawrence A. Farwell. Brain Fingerprinting Testing in the case of Harrington v. State, http://www.brainwavescience.com/LegalIssuesinAdmissibility.php, accessed May 5, 2008
• 17: Cram, J. R., Kasman, G. S., and Holtz, J. 1998. Introduction to Surface Electromyography. Aspen Publishers Inc.; Gaithersburg, Maryland.
• 18: Dimberg, U. 1997. Social fear and expressive reactions to social stimuli. Scandinavian Journal of Psychology, Volume 38, Issue 3 (Sep. 1997), 171--174.
• 19: Shaver, B. A. Jr, Brusilow, S. W., and Cooke, R. E. 1962. Origin of the galvanic skin response. Proceedings of the Society for Experimental Biology and Medicine, 110 (Jul. 1962), 559--564.
• 20: Fenz, W. D. and Epstein, S. 1967. Gradients of Physiological Arousal in Parachutists as a Function of an Approaching Jump. Psychosomatic Medicine, 29, 33--51.
• 21: Ranta-aho, P. O., Tarvainen M. P., Valkonen-Korhonen M., Georgiadis S. D., Lehtonen J., Niskanen J-P, and Karjalainen P. A. 2006. On Correlation Between Single-Trial ERP and GSR Responses: a Principal Component Regression Approach. 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, New York, August 30-September 3, 2006.
• 22: Gruzelier, J. H., and Brow T. D. 1985. Psychophysiological evidence for a state theory of hypnosis and susceptibility. Journal of Psychosomatic Research, Volume 29, Issue 3, 287--302.
• 23: SQLite. http://www.sqlite.org, accessed May 10, 2008.
• 24: BTRCS. http://www.sfu.ca/~cca73/batrcs_src.zip, accessed June 29, 2008.
• 25: Cover, T. and Hart, P. 1967. Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory. Volume 13, Issue 1 (Jan. 1967), 21--27.
• 26: Mitchell, T. M. 1997. Instance-based Learning. In Machine Learning. McGraw-Hill, Boston, MA, 230--240.
• 27: Luck, S. J. 2005. Averaging, Artifact Rejection, and Artifact Correction. In An Introduction to the Event-Related Potential Technique. The MIT Press, Cambridge, MA, 131--174.
• 28: Carlat, D. 2008. Mind Readers. In Wired Magazine (Jun. 2008), 120--128.

INDEX TERMS

Primary Classification:
  J. Computer Applications
  J.5 ARTS AND HUMANITIES
      Subjects: Fine arts

General Terms:
Algorithms, Design, Experimentation, Human Factors, Performance, Security

Keywords:
K-nearest neighbor, biofeedback, biometrics, interactive art, machine learning, physiological monitoring