Active detection of age groups based on touch interaction

Active detection of age groups based on touch interaction

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Biometrics — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

This article studies user classification into children and adults according to their interaction with touchscreen devices. The authors analyse the performance of two sets of features derived from the sigma-lognormal theory of rapid human movements and global characterisation of touchscreen interaction. The authors propose an active detection approach aimed to continuously monitor the user patterns. The experimentation is conducted on a publicly available database with samples obtained from 89 children between 3 and 6 years old and 30 adults. The authors have used support vector machines algorithm to classify the resulting features into age groups. The sets of features are fused at the score level using data from smartphones and tablets. The results, with correct classification rates over 96%, show the discriminative ability of the proposed neuromotor-inspired features to classify age groups according to the interaction with touch devices. In the active detection set-up, the authors’ method is able to identify a child using only four gestures in average.


    1. 1)
      • 1. CDS, Crystal Display System Ltd’. Available from, retrieved 10 September 2017.
    2. 2)
      • 2. Youtube, Youtube statistics’. Available from, retrieved 13 September 2016.
    3. 3)
      • 3. BBA: Mobile phone apps become the UK's number one way to bank’. Available from, retrieved 13 September 2016.
    4. 4)
      • 4. Morales, A., Fierrez, J., Tolosana, R., et al: ‘Keystroke biometrics ongoing competition’, IEEE Access, 2016, 4, pp. 77367746.
    5. 5)
      • 5. Kabali, H. K., Irigoyen, M.M., Nunez-Davis, R., et al: ‘Exposure and use of mobile media devices by young children’, Pediatrics, 2015, 136, (6), pp. 10441050.
    6. 6)
      • 6. Perera, P., Patel, V.M.: ‘Efficient and low latency detection of intruders in mobile active authentication’, IEEE Trans. Inf. Forensics Sec., 2018, 13, (6), pp. 13921405.
    7. 7)
      • 7. Patel, V.M., Chellappa, R., Chandra, D., et al: ‘Continuous user authentication on mobile devices: recent progress and remaining’, IEE Signal Process. Mag., 2016, 33, (4), pp. 4961.
    8. 8)
      • 8. Gonzalez-Sosa, E., Fierrez, J., Vera-Rodriguez, R., et al: ‘Facial soft biometrics for recognition in the wild: recent works, annotation and COTS evaluation’, IEEE Trans. Inf. Forensics Sec., 2018, 13, (8), pp. 20012014.
    9. 9)
      • 9. Cassidy, B., McKnight, L.: ‘Children's interaction with mobile touch-screen devices: experiences and guidelines for design’, Int. J. Mob. Hum. Comput. Interact., 2010, 2, pp. 118.
    10. 10)
      • 10. Aziz, N.A.A., Batmaz, F., Stone, R., et al: ‘Selection of touch gestures for children's applications’. Proc. of Science and Information Conf., IEEE, 2013, pp. 721726.
    11. 11)
      • 11. Anthony, L., Brown, Q., Nias, J., et al: ‘Interaction and recognition challenges in interpreting children's touch and gesture input on mobile devices’. Proc. of the 2012 ACM Int. Conf. Interactive tabletops and surfaces, ACM, Cambridge, MA, USA, 2012, pp. 225234.
    12. 12)
      • 12. Piaget, J., Inhelder, B.: ‘The psychology of the child’ (Basic Books, France, 1969), 5001).
    13. 13)
      • 13. O'Reilly, C., Plamondon, R.: ‘Development of a sigma–lognormal representation for on-line signatures’, Pattern Recognit., 2009, 42, (12), pp. 33243337.
    14. 14)
      • 14. AL-Showarah, S., AL-Jawad, N., Sellahewa, H.: ‘User-age classification using touch gestures on smartphones’, Int. J. Multidiscip. Stud., 2015, 2, (1), pp. 97109.
    15. 15)
      • 15. Bevan, C., Fraser, D.S.: ‘Different strokes for different folks? Revealing the physical characteristics of smartphone users from their swipe gestures’, Int. J. Human-Comput. Stud., 2016, 88, pp. 5161.
    16. 16)
      • 16. Vatavu, R.-D., Cramariuc, G., Schipor, D.M.: ‘Touch interaction for children aged 3 to 6 years: experimental findings and relationship to motor skills’, Int. J. Human-Comput. Stud., 2015, 74, pp. 5476.
    17. 17)
      • 17. Fierrez, J., Ortega-Garcia, J.: ‘On-line signature verification’, in ‘Handbook of biometrics’ (Springer-Verlag, Germany Berlin, 2008), pp. 189209.
    18. 18)
      • 18. Hernandez-Ortega, J., Morales, A., Fierrez, J., et al: ‘Detecting age groups using touch interaction based on neuromotor characteristics’, IET Electron. Lett., 2017, 53, (20), pp. 13491350.
    19. 19)
      • 19. Tolosana, R., Vera-Rodriguez, R., Fierrez, J., et al: ‘Benchmarking desktop and mobile handwriting across COTS devices: the e-BioSign biometric database’, PLOS ONE, 2017, 12, (5), pp. 117.
    20. 20)
      • 20. Vatavu, R.-D., Anthony, L., Brown, Q.: ‘Child or adult? Inferring smartphone users age group from touch measurements alone’, in ‘Human–computer interaction’ (Springer, Cham, Switzerland, 2015), pp. 19.
    21. 21)
      • 21. Fischer, A., Plamondon, A.: ‘A dissimilarity measure for on-line signature verification based on the sigmalognormal model’. Proc. of 17th Biennial Conf. of the Int. Graphonomics Society, Pointe-a-Pitre, Guadeloupe, 2015.
    22. 22)
      • 22. Plamondon, R., O'Reilly, C., Remi, C., et al: ‘The lognormal handwriter: learning, performing, and declining’, Front. Psychol., 2013, 4, pp. 945:1945:14.
    23. 23)
      • 23. Galbally, J., Plamondon, R., Fierrez, J.: ‘Synthetic on-line signature generation. Part I: methodology and algorithms’, Pattern Recognit., 2012, 45, pp. 26102621.
    24. 24)
      • 24. Ferrer, M.A., Diaz-Cabrera, M., Morales, A.: ‘Static signature synthesis: a neuromotor inspired approach for biometrics’, IEEE Trans. Pattern Anal. Mach. Intell., 2015, 37, (3), pp. 667680.
    25. 25)
      • 25. Duval, T., Rémi, C., Plamondon, R., et al: ‘Combining sigma-lognormal modeling and classical features for analyzing graphomotor performances in kindergarten children’, Hum. Mov. Sci., 2015, 43, pp. 183200.
    26. 26)
      • 26. Meulenbroek, R., VanGalen, P.G.: ‘The acquisition of skilled handwriting: discontinuous trends in kinematic variables’, in Colley, A.M., Beechs, J.R. (eds.): ‘Cognition and action in skilled behavior’ (North Holland, Amsterdam, 1998), pp. 273281.
    27. 27)
      • 27. Serwadda, A., Phoha, V.V., Wang, Z.: ‘Which verifiers work?: a benchmark evaluation of touch-based authentication algorithms’. Proc. of IEEE BTAS, Washington DC, USA, 2013, pp. 18.
    28. 28)
      • 28. Martinez-Diaz, M., Fierrez, J., Krish, R.P., et al: ‘Mobile signature verification: feature robustness and performance comparison’, IET Biometrics, 2014, 3, (4), pp. 267277.
    29. 29)
      • 29. Martinez-Diaz, M., Fierrez, J., Hangai, S.: ‘Signature features’, in ‘Encyclopedia of biometrics’ (Springer, Boston, MA, 2015), pp. 13751382.
    30. 30)
      • 30. Fierrez, J., Pozo, A., Martinez-Diaz, M., et al: ‘Benchmarking touchscreen biometrics for mobile authentication, IEEE Trans. Inf. Forensics Sec., 2018, 13, (11), pp. 27202733.
    31. 31)
      • 31. Fierrez, J., Morales, A., Vera-Rodriguez, R., et al: ‘Multiple classifiers in biometrics. Part 1: Fundamentals and review’, Information Fusion, 2018, 44, pp. 5764.

Related content

This is a required field
Please enter a valid email address