Skip to main content
SpringerLink
Log in

Automatic gender detection using on-line and off-line information

  • Short Paper
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

In this paper, the problem of classifying handwritten data with respect to gender is addressed. A classification method based on Gaussian Mixture Models is applied to distinguish between male and female handwriting. Two sets of features using on-line and off-line information have been used for the classification. Furthermore, we combined both feature sets and investigated several combination strategies. In our experiments, the on-line features produced a higher classification rate than the off-line features. However, the best results were obtained with the combination. The final gender detection rate on the test set is 67.57%, which is significantly higher than the performance of the on-line and off-line system with about 64.25 and 55.39%, respectively. The combined system also shows an improved performance over human-based classification. To the best of the authors’ knowledge, the system presented in this paper is the first completely automatic gender detection system which works on on-line data. Furthermore, the combination of on-line and off-line features for gender detection is investigated for the first time in the literature.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Notes

  1. eBeam System by Luidia, Inc., http://www.e-Beam.com.

  2. http://www.iam.unibe.ch/~fki/iamondb/.

  3. The test is available under http://www.iam.unibe.ch/~smueller/.

References

  1. Bandi K, Srihari SN (2005) Writer demographic classification using bagging and boosting. In: Proceedings of the 12th international graphonomics society conference, pp 133–137

  2. Beech JR, Mackintosh IC (2005) Do differences in sex hormones affect handwriting style? Evidence from digit ratio and sex role identity as determinants of the sex of handwriting. Pers Individ Dif 39(2):459–468

    Article  Google Scholar 

  3. Broom ME, Thompson B, et al. (1929) Sex differences in handwriting. J Appl Psychol 13:159–166

    Article  Google Scholar 

  4. Cha S-H, Srihari SN (2001) Apriori algorithm for sub-category classification analysis of handwriting. In: Proceedings of the 6th international conference on document analysis and recognition, pp 1022–1025

  5. Czyz J, Kittler J, Vandendorpe L (2004) Multiple classifier combination for face-based identity verification. Pattern Recognit 37(7):1459–1469

    Article  Google Scholar 

  6. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc B 39(1):1–38

    MATH  MathSciNet  Google Scholar 

  7. Hamid S, Loewenthal KM (1996) Inferring gender from handwriting in Urdu and English. J Social Psychol 136(6):778–782

    Article  Google Scholar 

  8. Hattori T, Izumi T, Kitajima H, Yamasaki T (2004) KANSEI information extraction from character patterns using a modified Fourier transform. In: Proceedings of the Sino-Japan symposium on KANSEI & artificial life, pp 36–39

  9. Hecker MR (1996) Die Untersuchung der Geschlechtsspezifität der Handschrift mittels Rechnergestützter Merkmalsextraktionsverfahren. PhD thesis, Humboldt-University, Berlin

  10. Huber RA (1999) Handwriting identification: facts and fundamentals. CRC Press, Boca Raton

  11. Jaeger S, Manke S, Reichert J, Waibel A (2001) Online handwriting recognition: the NPen++ recognizer. Int J Doc Anal Recognit 3(3):169–180

    Article  Google Scholar 

  12. Jiang X, Chen Y-F (2008) Facial image processing. In: Bunke H, Kandel A, Last M (eds.), Applied pattern recognition. Springer, Berlin

  13. Kuncheva LI (2004) Combining pattern classifiers: methods and algorithms. Wiley, London

  14. Liwicki M, Bunke H (2005) IAM-OnDB - an on-line English sentence database acquired from handwritten text on a whiteboard. In: Proceedings of the 8th internetional conference on document analysis and recognition, vol 2, pp 956–961

  15. Liwicki M, Schlapbach A, Bunke H, Bengio S, Mariéthoz J, Richiardi J (2006) Writer identification for smart meeting room systems. In: Proceedings of the 7th IAPR workshop on document analysis systems, vol 3872 of LNCS. Springer, pp 186–195

  16. Melin H, Koolwaaij JW, Lindberg J, Bimbot F (1998) A comparative evaluation of variance flooring techniques in HMM-based speaker verification. In: Proceedings of the 5th international conference on spoken language processing, pp 2379–2382

  17. Newhall SM (1926) Sex differences in handwriting. J Appl Psychol 10:151–161

    Article  Google Scholar 

  18. Reynolds DA, Quatieri TF, Dunn RB (2000) Speaker verification using adapted gaussian mixture models. Digit Signal Process 10:19–41

    Article  Google Scholar 

  19. Richiardi J, Ketabdar H, Drygajlo A (2005) Local and global feature selection for on-line signature verification. In: Proceedings of the 8th international conference on document analysis and recognition, pp 625–629

  20. Scheidat T, Wolf F, Vielhauer C (2006) Analyzing handwriting biometrics in metadata context. In: Proceedings of the 8th SPIE conference on the security, steganography, and watermarking of multimedia contents, vol 6072, pp 182–193

  21. Schlapbach A, Bunke H (2008) Off-line writer identification and verification using Gaussian mixture models. In: Marinai S, Fujisawa H (eds.), Machine learning in document analysis and recognition, vol 11. Springer, Berlin, pp. 409–428

  22. Schlapbach A, Liwicki M, Bunke H (2008) A writer identification system for on-line whiteboard data. Pattern Recognit 41:2381–2397

    Article  MATH  Google Scholar 

  23. Tenwolde H (1934) More on sex differences in handwriting. J Appl Psychol 18:705–710

    Article  Google Scholar 

  24. Wiskott L, Fellous J-M, Krüger N, von der Malsburg C (1995) Face recognition and gender determination. In: Proceedings of the international workshop on automatic face- and gesture-recognition, pp 92–97

  25. Wu B, Ai H, Huang C (2003) Audio- and video-based biometric person authentication, vol 2688 of LNCS, chapter LUT-based adaboost for gender classification. Springer, Berlin, pp 104–110

Download references

Acknowledgments

This work was supported by the Swiss National Science Foundation program “Interactive Multimodal Information Management (IM)2” in the Individual Project “Visual/Video Processing”, as part of NCCR. Special thanks go to Petra and Michael Liwicki, who provided assistance in getting references from the Humboldt-University, Berlin. Furthermore, we thank all volunteers who participated in the on-line classification test.

Author information

Authors and Affiliations

  1. Knowledge Management Department, German Research Center for AI (DFKI GmbH), Kaiserslautern, Germany

    Marcus Liwicki

  2. Institut für Informatik und angewandte Mathematik, Universität Bern, Neubrückstrasse 10, 3012, Bern, Switzerland

    Marcus Liwicki, Andreas Schlapbach & Horst Bunke

Authors
  1. Marcus Liwicki
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andreas Schlapbach
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Horst Bunke
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marcus Liwicki.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Liwicki, M., Schlapbach, A. & Bunke, H. Automatic gender detection using on-line and off-line information. Pattern Anal Applic 14, 87–92 (2011). https://doi.org/10.1007/s10044-010-0178-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-010-0178-6

Keywords

Search

Navigation