Abstract
The gender identification in handwritten documents becomes to gain importance for various writer authentication purposes. It provides information for anonymous documents for which we need to know if they were written by a Man or a Woman. In this work, we propose a system for writer’s gender classification that is based on local textural and gradient features. Especially our proposed features are Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP), which are successful in various pattern recognition applications. The classification step is achieved by SVM classifier. The results obtained on samples extracted from IAM dataset showed that the proposed features provide quite promising results.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Shulman, L.M.: Gender differences in Parkinson’s disease. Gend. Med. 4(1), 8–18 (2007)
Dorfberger, S., Adi-Japha, E., Karni, A.: Sex differences in motor performance and motor learning in children and adolescents: an increasing male advantage in motor learning and consolidation phase gains. Behav. Brain Res. 198(1), 165–171 (2009)
Voyer, D.: Sex differences in dichotic listening. Brain Cogn. J. 76, 245–255 (2011)
Bennett, S., Farrington, D.P., Huesmann, L.R.: Explaining gender differences in crime and violence: the importance of social cognitive skills. Aggression Violent Behav. 10(3), 263–288 (2005)
Lee, J.D., Lin, C.Y., Huang, C.H.: Novel features selection for gender classification. In: 2013 IEEE International Conference on Mechatronics and Automation (ICMA), pp. 785–790, August 2013
Beech, I.C., Mackintosh, J.R.: 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. Differ. 39, 459–468 (2005)
Cha, S.H., Srihari, S.N.: A priori algorithm for sub-category classification analysis of handwriting. In: Document Analysis and Recognition (IEEE, ed.), pp. 1022–1025, September 2001
Bandi, K.R., Srihari, S.N.: Writer demographic classification using bagging and boosting. In: Proceedings of International Graphonomics Society Conference, pp. 133–137 (2005)
Liwicki, M., Schlapbach, A., Bunke, H.: Automatic detection of gender and handedness from on-line handwriting. In: Conference of the International Graphonomics Society, pp. 179–183 (2007)
Liwicki, M., Schlapbach, A.: Automatic gender detection using on-line and off-line information. Pattern Anal. Appl. 14, 87–92 (2011)
Yilmaz, M., Yanikoglu, B., Tirkaz, C., Kholmatov, A.: Offline signature verification using classifier combination of hog and lbp features. In: 2011 International Joint Conference on Biometrics (IJCB), pp. 1–7, October 2011
Vargas, J., Ferrer, M., Travieso, C., Alonso, J.: Off-line signature verification based on grey level information using texture features. Pattern Recogn. 44(2), 375–385 (2011)
Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–59 (1996)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893, June 2005
Burges, C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disc. 2(2), 121–167 (1998)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Bouadjenek, N., Nemmour, H., Chibani, Y. (2017). Writer’s Gender Classification Using HOG and LBP Features. In: Chadli, M., Bououden, S., Zelinka, I. (eds) Recent Advances in Electrical Engineering and Control Applications. ICEECA 2016. Lecture Notes in Electrical Engineering, vol 411. Springer, Cham. https://doi.org/10.1007/978-3-319-48929-2_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-48929-2_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48928-5
Online ISBN: 978-3-319-48929-2
eBook Packages: EngineeringEngineering (R0)