Abstract
This paper evaluates the discriminant capabilities of face parts in gender recognition. Given the image of a face, a number of subimages containing the eyes, nose, mouth, chin, right eye, internal face (eyes, nose, mouth, chin), external face (hair, ears, contour) and the full face are extracted and represented as appearance-based data vectors. A greater number of face parts from two databases of face images (instead of only one) were considered with respect to previous related works, along with several classification rules. Experiments proved that single face parts offer enough information to allow discrimination between genders with recognition rates that can reach 86%, while classifiers based on the joint contribution of internal parts can achieve rates above 90%. The best result using the full face was similar to those reported in general papers of gender recognition (>95%). A high degree of correlation was found among classifiers as regards their capacity to measure the relevance of face parts, but results were strongly dependent on the composition of the database. Finally, an evaluation of the complementarity between discriminant information from pairs of face parts reveals a high potential to define effective combinations of classifiers.
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 subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Wu, J., Smith, W., Hancock, E.: Learning mixture models for gender classification based on facial surface normals. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds.) IbPRIA 2007. LNCS, vol. 4477, pp. 39–46. Springer, Heidelberg (2007)
Lapedriza, A., Marín-Jiménez, M., Vitrià, J.: Gender recognition in non controlled environments. In: Proc. of 18th International Conference on Pattern Recognition (ICPR 2006), Hong Kong. IEEE, Los Alamitos (2006)
Kawano, T., Kato, K., Yamamoto, K.: A comparison of the gender differentiation capability between facial parts. In: Proc. of 17th International Conference on Pattern Recognition (ICPR 2006), Cambridge, UK. IEEE, Los Alamitos (2004)
Buchala, S., Davey, N., Frank, R., Gale, T., Loomes, M., Kanargard, W.: Gender classification of faces images: The role of global and feature-based information. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds.) ICONIP 2004. LNCS, vol. 3316, pp. 763–768. Springer, Heidelberg (2004)
Marín-Jiménez, M., Pérez de la Blanca, N.: Empirical study of multi-scale filter banks for object categorization. In: Proc. of 18th International Conference on Pattern Recognition (ICPR 2006), Hong Kong. IEEE, Los Alamitos (2006)
Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20(3), 273–297 (1995)
Phillips, H., Moon, P., Rizvi, S.: The FERET evaluation methodology for face recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10) (2000)
Martinez, A., Benavente, R.: The AR face database. Technical Report 24, CVC (June 1998)
Jesorsky, O., Kirchberg, K., Frischholz, R.: Robust face detection using the hausdorff distance. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 90–95. Springer, Heidelberg (2001)
Messer, K., Matas, J., Kittler, J., Lüttin, J., Maitre, G.: XM2VTSDB: The extended M2VTS database. In: Audio and Video-based Biometric Person Authentication, AVBPA 1999, pp. 72–77 (1999)
Nainia, F.B., Mossb, J.P., Gillc, D.S.: The enigma of facial beauty: Esthetics, proportions, deformity, and controversy. American Journal of Orthodontics and Dentofacial Orthopedics 130(3), 277–282 (2006)
Oguz, O.: The proportion of the face in younger adults using the thumb rule of Leonardo da Vinci. Surgical and Radiologic Anatomy 18(2), 111–114 (1996)
Duin, R., Juszczak, P., Paclik, P., Pekalska, E., de Ridder, D., Tax, J.D.M.: PRTools4, A Matlab Toolbox for Pattern Recognition. Delft University of Technology, The Netherlands, 4.0 edn. (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Andreu, Y., Mollineda, R.A. (2008). The Role of Face Parts in Gender Recognition. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2008. Lecture Notes in Computer Science, vol 5112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69812-8_94
Download citation
DOI: https://doi.org/10.1007/978-3-540-69812-8_94
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69811-1
Online ISBN: 978-3-540-69812-8
eBook Packages: Computer ScienceComputer Science (R0)