© The Institution of Engineering and Technology
The authors present the author's results of using saccadic eye movements for biometric user verification. The method can be applied to computers or other devices, in which it is possible to include an eye movement camera system. Thus far, this idea has been little researched. As they have extensively studied eye movement signals for medical applications, they saw an opportunity for the biometric use of saccades. Saccades are the fastest of all eye movements, and are easy to stimulate and detect from signals. As signals measured from a physiological origin, the properties of eye movements (e.g. latency and maximum angular velocity) may contain considerable variability between different times of day, between days or weeks and so on. Since such variability might impair biometric verification based on saccades, they attempted to tackle this issue. In contrast to their earlier results, where they did not include such long intervals between sessions of eye movement recordings as in the present research, their results showed that – notwithstanding some variability present in saccadic variables – this variability was not considerable enough to essentially disturb or impair verification results. The only exception was a test series of very long intervals ∼16 or 32 months in length. For the best results obtained with various classification methods, false rejection and false acceptance rates were <5%. Thus, they conclude that saccadic eye movements can provide a realistic basis for biometric user verification.
References
-
-
1)
-
11. Zhang, Y., Rasku, J., Juhola, M.: ‘Biometric verification of subjects using saccade eye movements’, Int. J. Biometrics, 2012, 4, pp. 317–337 (doi: 10.1504/IJBM.2012.049736).
-
2)
-
6. Shen, J., Bao, S.-D., Yang, L.-C., Li, Y.: ‘The PLR-DTW method for ECG based biometric identification’. Proc. 33rd Ann. Int. Conf. IEEE EMBS, 2011, pp. 5248–5251.
-
3)
-
3. Sousedik, C., Busch, C.: ‘Presentation attack detection methods for fingerprint recognition systems: a survey’, IET Biometrics, 2014, 3, (4), pp. 219–233 (doi: 10.1049/iet-bmt.2013.0020).
-
4)
-
14. Schalén, L., Pyykkö, I., Juhola, M., Magnusson, M., Jäntti, V., Henriksson, N.-G.: ‘Intra-individual variation in oculomotor performance in man’, Acta Otolaryng., 1984, 406, pp. 212–217.
-
5)
-
13. Juhola, M., Zhang, Y., Rasku, J.: ‘Biometric verification of a subject through eye movements’, Comp. Biol. Med., 2013, 43, pp. 42–50 (doi: 10.1016/j.compbiomed.2012.10.005).
-
6)
-
15. Bollen, E., Bax, J., van Dijk, J.G., et al: ‘Variability of the main sequence’, Invest. Ophthal. Vis. Sci., 1993, 34, pp. 3700–3704.
-
7)
-
A.K. Jain ,
S. Prabhakar ,
L. Hong ,
S. Pankanti
.
Filterbank-based fingerprint matching.
IEEE Trans. Image Process.
,
5 ,
846 -
859
-
8)
-
A. Jain ,
A. Ross ,
S. Prabhakar
.
An introduction to biometric recognition.
IEEE Trans. Circuits Syst. Video Technol.
,
1 ,
4 -
20
-
9)
-
16. Smeets, J.B.J., Hooge, I.T.C.: ‘Nature of variability in saccades’, J. Neurophysiol., 2003, 90, pp. 12–20 (doi: 10.1152/jn.01075.2002).
-
10)
-
10. Zhang, Y., Laurikkala, J., Juhola, M.: ‘Biometric verification of a subject with eye movements, with special reference to temporal variability in saccades between a subject's measurements’, Int. J. Biometrics, 2014, 6, pp. 75–94 (doi: 10.1504/IJBM.2014.059643).
-
11)
-
J. Määttä ,
A. Hadid ,
M. Pietikäinen
.
Face spoofing detection from single images using texture and local shape analysis.
IET Biometrics
,
1 ,
3 -
10
-
12)
-
17. Juhola, M., Jäntti, V., Pyykkö, I.: ‘Effect of sampling frequencies on the maximum velocity of saccadic eye movements’, Biol. Cyber., 1985, 53, pp. 67–72 (doi: 10.1007/BF00337023).
-
13)
-
7. Baloh, R.W., Honrubia, V.: ‘Clinical neurophysiology of the vestibular system’ (F.A. Davis Company, 1979), pp. 75–76.
-
14)
-
12. Zhang, Y., Juhola, M.: ‘On biometric verification of a user by means of eye movement data mining’. Proc. Second Int. Conf. Adv. Inf. Mining Manag., 2012, pp. 85–90.
-
15)
-
9. Komogortsev, O.V., Holland, C.D., Karpov, A.: ‘Template aging in eye movement-driven biometrics’. Proc. SPIE 9075, Biometric and Surveillance Technology for Human and Activity Identification, 2014, .
-
16)
-
22. Mantasari, M.I., Günther, M., Wallace, R., Saedi, R., Marcel, S., Van Leeuwen, D.: ‘Score calibration in face recognition’, IET Biometrics, 2014, 3, (4), pp. 246–256 (doi: 10.1049/iet-bmt.2013.0066).
-
17)
-
8. Holland, C.D., Komogortsev, O.V.: ‘Complex eye movement pattern biometrics: the effects of environment and stimulus’, IEEE Trans. Inf. Forensics Sec., 2013, 8, pp. 2115–2126 (doi: 10.1109/TIFS.2013.2285884).
-
18)
-
18. Fransson, P.A., Patel, M., Magnusson, M., Berg, S., Almbladh, P., Gomez, S.: ‘Effects of 24-hour and 36-hour sleep deprivation on smooth pursuit and saccadic eye movements’, J. Vestib. Res., 2008, 18, pp. 209–222.
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