Abstract
Human faces demonstrate clear Sexual Dimorphism (SD) for recognizing the gender. Different faces, even of the same gender, convey different magnitude of sexual dimorphism. However, in gender classification, gender has been interpreted discretely as either male or female. The exact magnitude of the sexual dimorphism in each gender is ignored. In this paper, we propose to evaluate the SD magnitude, using the ratio of votes from the Random Forest algorithm performed on 3D geometric features related to the face morphology. Then, faces are separated into a Low-SD group and a High-SD group. In the Intra-group experiments, when the training is performed with scans of similar SD magnitude than the testing scan, the classification accuracy improves. In Inter-group experiments, the scans with low magnitude of SD demonstrate higher gender discrimination power than the ones with high SD magnitude. With a decision-level fusion method, our method achieves 97.46 % gender classification rate on the 466 earliest 3D scans of FRGCv2 (mainly neutral), and 97.18 % on the whole FRGCv2 dataset (with expressions).
Chapter PDF
Similar content being viewed by others
References
Hall, M.A.: Correlation-based feature subset selection for machine learning. Ph.D thesis, Department of Computer Science, University of Waikato (1999)
Ballihi, L., Ben Amor, B., Daoudi, M., Srivastava, A., Aboutajdine, D.: Boosting 3D-geometric features for efficient face recognition and gender classification. IEEE Transactions on Information Forensics and Security 7, 1766–1779 (2012)
Baudouin, J.Y., Gallay, M.: Is face distinctiveness gender based? Journal of Experimental Psychology: Human Perception and Performance 32(4), 789 (2006)
Ben Amor, B., Drira, H., Berretti, S., Daoudi, M., Srivastava, A.: 4d facial expression recognition by learning geometric deformations. IEEE Transactions on Cybernetics, February 2014
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Bruce, V., Burton, A.M., Hanna, E., Healey, P., Mason, O., Coombes, A., Fright, R., Linney, A.: Sex discrimination: How do we tell the difference between male and female faces? Perception. 22(2), 131–152 (1993)
Drira, H., Ben Amor, B., Srivastava, A., Daoudi, M., Slama, R.: 3d face recognition under expressions, occlusions, and pose variations. IEEE Transactions on Pattern Analysis and Machine Intelligence. 35, 2270–2283 (2013)
Drira, H., Ben Amor, B., Daoudi, M., Srivastava, A., Berretti, S.: 3d dynamic expression recognition based on a novel deformation vector field and random forest. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 1104–1107. IEEE (2012)
Du, L., Zhuang, Z., Guan, H., Xing, J., Tang, X., Wang, L., Wang, Z., Wang, H., Liu, Y., Su, W., et al.: Head-and-face anthropometric survey of chinese workers. Annals of occupational hygiene 52(8), 773–782 (2008)
Geng, X., Zhou, Z.H., Smith-Miles, K.: Automatic age estimation based on facial aging patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 2234–2240 (2007)
Gilani, S.Z., Shafait, F., Ajmal, M.: Biologically significant facial landmarks: how significant are they for gender classification?. In: DICTA, pp. 1–8 (2013)
Guillaume, V., Harold, H., Eric, V.B.: Linking the structure and perception of 3d faces: gender, ethnicity, and expressive posture. In: International Conference on Audio-Visual Speech Processing (AVSP) (2003)
Han, X., Ugail, H., Palmer, I.: Gender classification based on 3D face geometry features using svm. In: CyberWorlds, pp. 114–118 (2009)
Hill, H., Bruce, V., Akamatsu, S.: Perceiving the sex and race of faces: the role of shape and colour. Proceedings of the Royal Society of London Series B Biological Sciences 261(1362), 367–373 (1995)
Hu, Y., Yan, J., Shi, P.: A fusion-based method for 3D facial gender classification. Computer and Automation Engineering (ICCAE) 5, 369–372 (2010)
Hu, Y., Fu, Y., Tariq, U., Huang, T.S.: Subjective experiments on gender and ethnicity recognition from different face representations. In: Boll, S., Tian, Q., Zhang, L., Zhang, Z., Chen, Y.-P.P. (eds.) MMM 2010. LNCS, vol. 5916, pp. 66–75. Springer, Heidelberg (2010)
Huang, D., Ding, H., Wang, C., Wang, Y., Zhang, G., Chen, L.: Local circular patterns for multi-modal facial gender and ethnicity classification. Image and Vision Computing (0) (2014)
Hunter, W.S., Garn, S.M.: Disproportionate sexual dimorphism in the human face. American Journal of Physical Anthropology 36(1), 133–138 (1972)
Huynh, T., Min, R., Dugelay, J.: An efficient lbp-based descriptor for facial depth images applied to gender recognition using rgb-d face data. In: ACCV 2012, Workshop on Computer Vision with Local Binary Pattern Variants (2012)
Jones, B.C., DeBruine, L.M., Little, A.C.: The role of symmetry in attraction to average faces. Perception & Psychophysics 69(8), 1273–1277 (2007)
Kohavi, R.: Wrappers for performance enhancement and oblivious decision graphs. Ph.D thesis, Stanford University (1995)
Komori, M., Kawamura, S., Ishihara, S.: Effect of averageness and sexual dimorphism on the judgment of facial attractiveness. Vision Research 49(8), 862–869 (2009)
Little, A., Jones, B., Waitt, C., Tiddeman, B., Feinberg, D., Perrett, D., Apicella, C., Marlowe, F.: Symmetry is related to sexual dimorphism in faces: data across culture and species. PLoS One 3(5), e2106 (2008)
Little, A.C., Jones, B.C., DeBruine, L.M., Feinberg, D.R.: Symmetry and sexual dimorphism in human faces: interrelated preferences suggest both signal quality. Behavioral Ecology 19(4), 902–908 (2008)
Liu, Y., Palmer, J.: A quantified study of facial asymmetry in 3D faces. In: Analysis and Modeling of Faces and Gestures, pp. 222–229 (2003)
Lu, X., Chen, H., Jain, A.K.: Multimodal facial gender and ethnicity identification. In: International Conference on Advances in Biometrics, pp. 554–561 (2006)
Phillips, P., Flynn, P., Scruggs, T., Bowyer, K., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the face recognition grand challenge. Computer Vision and Pattern Recognition 1, 947–954 (2005)
Rhodes, G., Zebrowitz, L.A., Clark, A., Kalick, S.M., Hightower, A., McKay, R.: Do facial averageness and symmetry signal health? Evolution and Human Behavior 22(1), 31–46 (2001)
Shuler, J.T.: Facial sexual dimorphism and judgments of personality: a literature review. Issues 6(1) (2012)
Smith, F.G., Jones, B.C., DeBruine, L.M., Little, A.C.: Interactions between masculinity-femininity and apparent health in face preferences. Behavioral Ecology 20(2), 441–445 (2009)
Srivastava, A., Klassen, E., Joshi, S., Jermyn, I.: Shape analysis of elastic curves in euclidean spaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 33, 1415–1428 (2011)
Steven, W., Randy, T.: Facial masculinity and fluctuating asymmetry. Evolution and Human Behavior 24(4), 231–241 (2003)
Toderici, G., O’Malley, S., Passalis, G., Theoharis, T., Kakadiaris, I.: Ethnicity- and gender-based subject retrieval using 3-D face-recognition techniques. International Journal of Computer Vision 89, 382–391 (2010)
Wang, X., Kambhamettu, C.: Gender classification of depth images based on shape and texture analysis. In: Global Conference on Signal and Information Processing (GlobalSIP), pp. 1077–1080. IEEE (2013)
Wu, J., Smith, W., Hancock, E.: Gender classification using shape from shading. In: International Conference on Image Analysis and Recognition, pp. 499–508 (2007)
Xia, B., Ben Amor, B., Drira, H., Daoudi, M., Ballihi, L.: Gender and 3D facial symmetry: what’s the relationship?. In: IEEE Conference on Automatic Face and Gesture Recognition (2013)
Xia, B., Ben Amor, B., Huang, D., Daoudi, M., Wang, Y., Drira, H.: Enhancing gender classification by combining 3d and 2d face modalities. In: European Signal Processing Conference (EUSIPCO) (2013)
Young, J.: Head and face anthropometry of adult us civilians (1993)
Zhuang, Z., Bradtmiller, B.: Head and face anthropometric survey of us respirator users. Journal of Occupational and Environmental Hygiene 2(11), 567–576 (2005)
Zhuang, Z., Landsittel, D., Benson, S., Roberge, R., Shaffer, R.: Facial anthropometric differences among gender, ethnicity, and age groups 54(4), 391–402 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Xia, B., Ben Amor, B., Daoudi, M. (2015). Exploring the Magnitude of Human Sexual Dimorphism in 3D Face Gender Classification. In: Agapito, L., Bronstein, M., Rother, C. (eds) Computer Vision - ECCV 2014 Workshops. ECCV 2014. Lecture Notes in Computer Science(), vol 8926. Springer, Cham. https://doi.org/10.1007/978-3-319-16181-5_53
Download citation
DOI: https://doi.org/10.1007/978-3-319-16181-5_53
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16180-8
Online ISBN: 978-3-319-16181-5
eBook Packages: Computer ScienceComputer Science (R0)