Skip to main content
Log in

Biometric Applications Related to Human Beings: There Is Life beyond Security

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

The use of biometrics has been successfully applied to security applications for some time. However, the extension of other potential applications with the use of biometric information is a very recent development. This paper summarizes the field of biometrics and investigates the potential of utilizing biometrics beyond the presently limited field of security applications. There are some synergies that can be established within security-related applications. These can also be relevant in other fields such as health and ambient intelligence. This paper describes these synergies. Overall, this paper highlights some interesting and exciting research areas as well as possible synergies between different applications using biometric information.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Notes

  1. It should be noticed that not all these findings proved to be strong enough, as, for example, [10, 13] disconfirmed the existence of an autonomic specificity and distinctive ANS’s activity patterns for each basic emotion.

References

  1. Aarts E, Harwig R, Schuurmans M. Ambient intelligence. In: Denning PJ, editor. The Invisible future: the seamless integration of technology into everyday life. New York: McGraw-Hill; 2001. p. 235–50.

  2. Abdullah R. Intelligent methods for complex systems control engineering. PhD thesis with Dr Amir Hussain. UK: The University of Stirling; 2007.

  3. Abdullah R, Hussain A, Warwick K, Zayed A. Autonomous intelligent cruise control using a novel multiple-controller framework incorporating fuzzy-logic-based switching and tuning. Neurocomputing. 2008;71:2727–41.

    Article  Google Scholar 

  4. Abel A, Hussain A, Nguyen QD, Ringeval F, Chetouani M, Milgram M. Maximising audiovisual correlation with automatic lip tracking and vowel based segmentation. In: Biometric ID, editor. Management and multimodal communication. Berlin: Springer; 2009. p. 65–72.

    Chapter  Google Scholar 

  5. Ackermann H, Hertich I, Daum I, Scharf G, Spieker S. Kinematic analysis of articularoty movements in central motor disorders. Mov Disord. 1997;12(6):1019–27.

    Article  PubMed  CAS  Google Scholar 

  6. Almajai I, Milner B. Effective visually-derived Wiener filtering for audio-visual speech processing. In: Proceedings of the interspeech. Brighton, UK; 2009.

  7. Almajai I, Milner B. Maximising audio-visual speech correlation. In: Proceedings of the AVSP. 2007.

  8. Barker JP, Berthommier F. Evidence of correlation between acoustic and visual features of speech. In: Proceedings of the ICPhS ‘99. 1999; p. 199–202.

  9. Beatty WW, Orbelo DM, Sorocco KH, Ross ED. Comprehension of affective prosody in multiple sclerosis. Multiple Scler J. 2003;9(2):148–53.

    Article  Google Scholar 

  10. Bermond B, Nieuwenhuyse B, Fasotti L, Schuerman J. Spinal cord lesions, peripheral feedback, and intensities of emotional feelings. Cognit Emot. 1991;5:201–20.

    Article  Google Scholar 

  11. Bidet-Ildei C, Pollak P, Kandel S, Fraix V, Orliaguet J-P. Handwriting in patients with parkinson disease: effect of l-dopa and stimulation of the sub-thalamic nucleus on motor anticipation. Hum Mov Sci. 2011;30(4):783–91.

    Article  PubMed  Google Scholar 

  12. Birren JE, Botwinick J. The relation of writing speed to age and to the senile psychoses. J Consult Psychol. 1951;15(3):243–9.

    Article  PubMed  CAS  Google Scholar 

  13. Caccioppo JT, Klein DJ, Bernston GC, Hatfield E. The psychophysiology of emotion. In: Lewis JM, Haviland-Jones M, editors. Handbook of emotion. New York: Guilford Press; 1993. p. 119–42.

    Google Scholar 

  14. Cambria E, Hussain A, Havasi C, Eckl C. Sentic computing: exploitation of common sense for the development of emotionsensitive systems. Lect Notes Comput Sci. 2010;5967:148–56.

    Article  Google Scholar 

  15. Cambria E, Hussain A, Durrani T, Havasi C, Eckl C, Munro J. Sentic computing for patient centered applications. In: 10th IEEE international conference on signal processing (ICSP), 2010. p. 1279–82.

  16. Cambria E, Hussain A, Eckl C. Bridging the gap between structured and unstructured health-care data through semantics and sentics. In: Proceedings of the ACM WebSci'11; 2011. p. 1–4.

  17. Cambria E, Hupont I, Hussain A, Cerezo E, Baldassarri S. Sentic avatar: multimodal affective conversational agent with common sense. LNCS, vol. 6456. Berlin, Heidelberg: Springer; 2011. p. 81–95.

  18. Cifani S, Abel A, Hussain A, Squartini S, Piazza F. An investigation into audiovisual speech correlation in reverberant noisy environments. Lect Notes Comput Sci. 2009;5641:331–43.

    Article  Google Scholar 

  19. Chan D, Fox NC, Scahill RI, Crum WR, Whitwell JL, Leschziner G, Rossor AM, Stevens JM, Ciplolotti L, Rossor MN. Patterns on temporal lobe atrophy in semantic dementia and Alzheimer’s disease. Ann Neurol. 2001;49:433–42.

    Article  PubMed  CAS  Google Scholar 

  20. Delaherche E, Chetouani M. Multimodal coordination: exploring relevant features and measures. In: SSPW '10 Proceedings of the 2nd international workshop on Social signal processing; 2010. p. 47–52.

  21. Eichhorn TE, Gasser T, Mai N, Marquardt C, Arnold G, Schwarzy J, Oertel WH. Computational analysis of open loop handwriting movements in Parkinson’s disease: a rapid method to detect dopamimetic effects. Mov Disord. 1996;11(3):289–97.

    Article  PubMed  CAS  Google Scholar 

  22. Ekman P. An argument for basic emotions. Cognit Emot. 1992;6:169–200.

    Article  Google Scholar 

  23. Ericsson K, Forssell LG, Holmén K, Viitanen M, Winblad B. Copying and handwriting ability in the screening of cognitive dysfunction in old age. Arch Gerontol Geriatr. 1996;22:103–21.

    Article  PubMed  CAS  Google Scholar 

  24. Esposito A. The amount of information on emotional states conveyed by the verbal and nonverbal channels: some perceptual data. In: Stilianou Y, et al., editors. Progress in nonlinear speech processing. Lecture notes in computer science, vol. 4392. Berlin: Springer; 2007. p. 45–264.

    Google Scholar 

  25. Esposito A. Affect in multimodal information. In: Tao J, Tan T, editors. Affective information processing. Heidelberg: Springer; 2008. p. 211–34.

    Google Scholar 

  26. Esposito A. The perceptual and cognitive role of visual and auditory channels in conveying emotional information. Cogn Comput J. 2009;1(2):268–78.

    Article  Google Scholar 

  27. Faruk A, Turan N. Handwritten changes under the effect of alcohol. Forensic Sci Int. 2003;132(3):201–10.

    Article  Google Scholar 

  28. Ferrand C. Harmonics-to-noise ratio: an indexing of vocal aging. J Voice. 2002;16(4):480–7.

    Article  PubMed  Google Scholar 

  29. Foley RG, Miller L. The effects of marijuana and alcohol usage on handwriting. Forensic Sci Int. 1979;14(3):159–64.

    Article  PubMed  CAS  Google Scholar 

  30. Folstein MF, Folstein SE, McHugh PR. Mini mental state: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12:189–98.

    Article  PubMed  CAS  Google Scholar 

  31. Forbes KE, Shanks MF, Venneri A. The evolution of dysgraphia in Alzheimer’s disease. Brain Res Bull. 2004;63:19–24.

    Article  PubMed  Google Scholar 

  32. Fotiou DF, Stergiou V, Tsiptsios D, Lithari C, Nakou M, Karlovasitou A. Cholinergic deficiency in Alzheimer’s and Parkinson’s disease: evaluation with pupillometry. Int J Psychophysiol. 2009;73(2):143–9.

    Article  PubMed  CAS  Google Scholar 

  33. Fountoulakis K, Fotiou F, Iacovides A, Tsiptsios J, Goulas A, Tsolaki M, Ierodiakonou C. Changes in pupil reaction to light in melancholic patients. Int J Psychophysiol. 1999;31(2):121–8. ISSN 0167-8760.

    Google Scholar 

  34. Frijda NH. Moods, emotion episodes, and emotions. In: Lewis JM, Haviland-Jones M, editors. Handbook of emotion. New York: Guilford Press; 1993. p. 381–402.

    Google Scholar 

  35. Girin L, Schwartz JL, Feng G. Audio-visual enhancement of speech in noise. J Acoust Soc Am. 2001;109(6):3007–20.

    Article  PubMed  CAS  Google Scholar 

  36. Gobermana AM, Coelho C. Acoustic analysis of Parkinsonian speech I: speech characteristics and L-Dopa therapy. NeuroRehabilitation. 2002;17:237–46.

    Google Scholar 

  37. Gobermana AM, Coelho C. Acoustic analysis of Parkinsonian speech II: L-Dopa related fluctuations and methodological issues. NeuroRehabilitation. 2002;17:247–54.

    Google Scholar 

  38. Goecke R, Potamianos G, Neti C. Noisy audio feature enhancement using audio-visual speech data. In: Acoustics, speech, and signal processing, Proceedings (ICASSP’02), vol. 2. IEEE International Conference; 2002. p. 2025–2028.

  39. Gorriz JM, Segovia F, Ramirez J, Lassl A, Salas-Gonzalez D. GMM based SPECT image classification for the diagnosis of Alzheimer’s disease. Appl Soft Comput. 2011;11:2313–25.

    Article  Google Scholar 

  40. Groves-Wright K, Neils-Strunjas J, Burnett R, O’Neill MJ. A comparison of verbal and written language in Alzheimer’s disease. J Commun Disord. 2004;37(2):109–30.

    Article  PubMed  Google Scholar 

  41. Gustaw K, Gonet W. Speech disorders in multiple system atrophy of parkinson type. Clin Res. 2008;1(2):185–8.

    Google Scholar 

  42. Heinik J, Werner P, Dekel T, Gurevitz I, Rosenblum S. Computerized kinematic analysis of the clock drawing task in elderly people with mild major depressive disorder: an exploratory study. Int Psychogeriatr. 2010;22(3):479–88.

    Article  PubMed  Google Scholar 

  43. Holz FG, Piguet B, Minassian DC, Bird AC, Weale RA. Decreasing stromal iris pigmentation as a risk factor for age-related macular degeneration. Am J Ophthalmol. 1994;117(1):19–23.

    PubMed  CAS  Google Scholar 

  44. Iliadou V, Kaprinis S. Clinical psychoacoustics in Alzheimer’s disease central auditory processing disorders and speech deterioration. Ann Gen Hospital Psychiatr. 2003;2:12.

    Article  Google Scholar 

  45. Illán IA, Górriz JM, Ramírez J, Salas-Gonzalez D, López MM, Segovia F, Chaves R, Gómez-Rio M, Puntonet CG, the Alzheimer’s Disease Neuroimaging Initiative. 18F-FDG PET imaging analysis for computer aided Alzheimer’s diagnosis. Inf Sci. 2011;181(4):903–16.

    Article  Google Scholar 

  46. Izard CE. Innate and universal facial expressions: evidence from developmental and cross-cultural research. Psycholog Bull. 1994;115:288–99.

    Article  CAS  Google Scholar 

  47. Kempler D, Curtiss S. Catherine jackson “synthactic preservation in Alzheimer’s disease”. J speech Hearing Res. 1987;30:343–50.

    PubMed  CAS  Google Scholar 

  48. Kushki A, Chau T, Anagnostou E. Handwriting difficulties in children with autism spectrum disorders: a scoping review. J Autism Dev Disord. 2011;41(12):1706–16.

    Article  PubMed  Google Scholar 

  49. Lee L, Grimson WEL. Gait analysis for recognition and classification. Automatic face and gesture recognition, 2002. In: Proceedings of the fifth IEEE international conference; 2002. p. 148–55.

  50. Levenson RW. Human emotion: a functional view. In: Ekman PP, Davidson RJ, editors. The nature of emotion: fundamental questions. New York: Oxford University Press; 1994. p. 123–6.

    Google Scholar 

  51. Liu R, Zhou J, Liu M, Hou X. A wearable acceleration sensor system for gait recognition. In: 2nd IEEE Conference on industrial electronics and applications, ICIEA 2007; 2007. p. 2654–59.

  52. Liu L, Popescu M, Rantz M, Skubic M, Cuddihy P, Yardibi T. Automatic fall detection based on Doppler radar motion signature. In: 5th International conference on pervasive computing technologies for healthcare; 2011. p. 222–5.

  53. Llau Arcusa MJ, Gonzalez Alvarez J. Medida de la inteligibilidad en el habla disaártrica. Rev Logop Foniatr Audiol. 2004;24:33–43.

    Google Scholar 

  54. Maltoni D, Maio D, Jain AK, Prabhakar S. Handbook of fingerprint recognition. 1st ed. New York: Springer; 2003.

    Google Scholar 

  55. Maternaghan C, Turner KJ. A component framework for telecare and home automation. In: CCNC'10 Proceedings of the 7th IEEE conference on consumer communications and networking conference; 2009. p. 886–870.

  56. McGurk H, MacDonald J. Hearing lips and seeing voices. Nature. 1976;264:746–8.

    Article  PubMed  CAS  Google Scholar 

  57. Mekyska J, Smekal Z, Kostalova M, Mrackova M, Skutilova S, Rektorova I. Motor aspects of speech imparment in Parkinson’s disease and their assessment. Cesk Slov Neurol N. 2011;74:662–8.

    Google Scholar 

  58. Moreau C, Ozsancak C, Blatt J-L, Derambure P, Destee A, Defebvre L. Oral festination in parkinson’s disease: biomechanical analysis and correlation with festination and freezing of gait. Mov Disord. 2007;22(10):1503–6.

    Article  PubMed  Google Scholar 

  59. Nagulic M, Davidovic J, Nagulic I. Parkinsonian voice acoustic analysis in real-time after stereotactic thalamotomy. Stereotact Funct Neurosurg. 2005;83(2–3):115–21.

    Article  PubMed  Google Scholar 

  60. Neils-Strunjas J, Groves-Wright K, Mashima P, Harnish S. Dyspgraphia in Alzheimer’s disease: a review for clinical and research purposes. J speech Lang Hearing Res. 2006;49(6):1313–30.

    Article  Google Scholar 

  61. Oatley K, Jenkins JM. Understanding emotions. 2nd ed. Oxford: Blackwell; 2006.

    Google Scholar 

  62. Ohn TG, Braak H. Auditory brainstem nuclei in Alzheimer’s disease. Neurosci Lett. 1989;2:60–3.

    Google Scholar 

  63. Ozsancak C, Auzou P, Jan M, Defebvre L, Derambure P, Destee A. The place of perceptual analysis of dysarthria in the differential diagnosis of corticobasal degeneration and Parkinson’s disease. J Neurol. 2006;253(1):92–7.

    Article  PubMed  Google Scholar 

  64. Panksepp J. Emotions as natural kinds within the mammalian brain. In: Lewis JM, Haviland-Jones M, editors. Handbook of emotions. 2nd ed. New York: Guilford Press; 2000. p. 137–56.

    Google Scholar 

  65. Phillips JG, Ogeil RP, Muller F. Alcohol consumption and handwriting: a kinematic analysis. Hum Mov Sci. 2009;28:619–32.

    Article  PubMed  Google Scholar 

  66. Plutchik R. Emotions as adaptive reactions: implications for therapy. Psychoanal Rev LIII. 1966;2:105–10.

    Google Scholar 

  67. Ramlee RA, Ranjit S. Using iris recognition algorithm, detecting cholesterol presence. In: International conference on information management and engineering; 2009. p. 714–7.

  68. Rantz MJ, Skubic M, Koopman RJ, Phillips L, Alexander GL, Miller SJ, Guevara RD. Using sensor networks to detect urinary tract infections in older adults. In: Proceedings of the IEEE 13th international conference on e-health networking, applications and services. 2011.

  69. Rapcan V, D’Arcy S, Yeap S, Afzal N, Thakore J, Reilly RB. Acoustic and temporal analysis of speech: a potential biomarker for schizophrenia. Med Eng Phys. 2010;32(9):1074–9.

    Article  PubMed  Google Scholar 

  70. Ringeval F, Demouy J, Szaszak G, Chetouan M, Robel L, Xavier J, Cohen D, Plaza M. Automatic intonation recognition for the prosodic assessment of language impaired children. IEEE Trans Audio Speech Lang Process. 2011;19(5):1328–42.

    Article  Google Scholar 

  71. Roberts VJ, Ingram SM, Lamar M. Prosody impairment and associated affective and behavioral disturbances in Alzheimer’s disease. Neurology. 1996;47:1482–8.

    Article  PubMed  CAS  Google Scholar 

  72. Rosenblum S, Parush S, Weiss PL. The in air phenomenon: temporal and spatial correlates of the handwriting process. Percept Mot Skills. 2003;96(3):933–54.

    Article  PubMed  Google Scholar 

  73. Russell JA. A circumplex model of affect. J Pers Soc Psychol. 1980;39:1161–78.

    Article  Google Scholar 

  74. Sargin ME, Yemez Y, Erzin E, Tekalp AM. Audiovisual synchronization and fusion using canonical correlation analysis”. IEEE Trans Multimed. 2007;9(7):1396–403.

    Article  Google Scholar 

  75. Saunder-Pullman R, Derbym C, Stanley K, Floyd A, Bressman S, Lipton RB, Deligtisch A, Severt L, Qiping Yu, Kurtis M, Pullman SL. Validity of spiral analysis in early Parkinson’s disease. Mov Disord. 2008;23(4):531–7.

    Article  Google Scholar 

  76. Scherer KR, Banse R, Wallbott HG. Emotion inferences from vocal expression correlate across languages and cultures. J Cross Cult Psychol. 2001;32:76–92.

    Article  Google Scholar 

  77. Schlosberg H. Three dimensions of emotion. Psychol Rev. 1953;61(2):81–8.

    Article  Google Scholar 

  78. Sesa E, Faundez-Zanuy M. Biometric recognition using online uppercase handwritten text. Pattern Recognit. 2012;45(1):128–44.

    Article  Google Scholar 

  79. Sesa E, Faundez-Zanuy M, Mekyska J. An information analysis of in-air and on-surface trajectories in online handwriting. Cognit Comput. 2012;4:195–205.

    Article  Google Scholar 

  80. Skodda S, Schlegel U. Speech rate and rhythm in parkinson’s disease. Mov Disord. 2008;23(7):985–92.

    Article  PubMed  Google Scholar 

  81. Sodoyer D, Schwartz JL, Girin L, Klinkisch J, Jutten C. Separation of audio-visual speech sources: a new approach exploiting the audio-visual coherence of speech stimuli. EURASIP J Appl Signal Process. 2002;11(1):1165–73.

    Google Scholar 

  82. Sodoyer D, Girin L, Jutten C, Schwartz JL. Developing an audio-visual speech source separation algorithm. Speech Commun. 2004;44(1–4):113–25.

    Article  Google Scholar 

  83. Stewart C, Winfield L, Junt A, Bressman SB, Fahn S, Blitzer A, Brin MF. Speech dysfunction in early Parkinson’s disease. Mov Disord. 1995;10(5):562–5.

    Article  PubMed  CAS  Google Scholar 

  84. Sunderland T, Hill JL, Mellow AM, et al. Clock drawing in Alzheimer’s disease: a novel measure of dementia severity. J Am Geriatr Soc. 1989;37:725–9.

    PubMed  CAS  Google Scholar 

  85. Trombetti A, Hars M, Herrmann FR, Kressig RW, Ferrari S, Rizzoli R. Effect of music-based multitask training on gait, balance, and fall risk in elderly people: a randomized controlled trial. Arch Intern Med. 2011;171(6):525–33.

    Article  PubMed  Google Scholar 

  86. Stone EE, Skubic M. Evaluation of an inexpensive depth camera for passive in-home fall risk assessment. In: Proceedings of the 4th International conference on pervasive computing technologies for healthcare. Dublin; 2011.

  87. Sumby WH, Pollack I. Visual contribution to speech intelligibility in noise. J Acoust Soc Am. 1954;26(2):212–5.

    Article  Google Scholar 

  88. Tripolitia EE, Fotiadisb DI, Argyropoulou M. A supervised method to assist the diagnosis and monitor progression of Alzheimer’s disease using data from an fMRI experiment. Artif Intell Med. 2011;53(1):35–45.

    Article  Google Scholar 

  89. Tucha O, Mecklinger L, Thome J, Reiter A, Alders GL, Sartor H, Naumann M, Lange KW. Kinematic analysis of dopaminergic effects on skilled handwriting movements in Parkinson’s disease. J Neural Transm. 2006;113:609–23.

    Article  PubMed  CAS  Google Scholar 

  90. Tucha O, Mecklinger L, Walitza S, Lange KW. The effect of caffeine on handwriting movements in skilled writers. Hum Mov Sci. 2006;25(4–5):523–35.

    Article  PubMed  Google Scholar 

  91. Verghese J, Holtzer R, Lipton RB, Wang C. Quantitative gait markers and incident fall risk in older adults. J Gerontol Ser A Biol Sci Med Sci. 2009;64A(8):896–901.

    Article  Google Scholar 

  92. Viñals Carrera F, Puente Balsells ML. “Grafología criminal”, capítulo 3, alteraciones neurológicas y biológicas. Editorial Herder, 2009.

  93. Warkentin S, Erikson C, Janciauskiene S. rCBF pathology in Alzheimer’s disease is associated with slow processing speed. Neuropsychologia. 2008;46(5):1193–200.

    Article  PubMed  CAS  Google Scholar 

  94. Werner Perla, Rosenblum Sara, Bar-On Gady, Heinik J, Korczyn A. Handwriting process variables discriminating mild Alzheimer’s disease and mild cognitive impairment”. J Gerontol. 2006;61B(4):228–36.

    Google Scholar 

  95. Woodward J. Biometrics: identifying law and policy concerns. In: Jain AK, Bolle RM, Pankanti S, editors. Biometrics: personal identification in networked society. New York: Springer; 2005. p. 385–405.

  96. Zelkha E, Epstein B, Birrell S, Dodsworth C. From devices to “ambient intelligence”. Digital Living Room Conference, June 1998. http://www.epstein.org/brian/ambient_intelligence/DLR%20Final%20Internal.ppt.

Download references

Acknowledgments

This work was supported by FEDER and MEC, TEC2009-14123-C04-04. SIX (CZ.1.05/2.1.00/03.0072), CZ.1.07/2.3.00/20.0094, VG20102014033, GACR 102/12/1104 and KONTAKT ME10123. We also thank Francesc Viñals and Mari Luz Puente for providing the examples in Figs. 12 and 13.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Marcos Faundez-Zanuy.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Faundez-Zanuy, M., Hussain, A., Mekyska, J. et al. Biometric Applications Related to Human Beings: There Is Life beyond Security. Cogn Comput 5, 136–151 (2013). https://doi.org/10.1007/s12559-012-9169-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-012-9169-9

Keywords

Navigation