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Ethnicity- and Gender-based Subject Retrieval Using 3-D Face-Recognition Techniques

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Abstract

While the retrieval of datasets from human subjects based on demographic characteristics such as gender or race is an ability with wide-ranging application, it remains poorly-studied. In contrast, a large body of work exists in the field of biometrics which has a different goal: the recognition of human subjects. Due to this disparity of interest, existing methods for retrieval based on demographic attributes tend to lag behind the more well-studied algorithms designed purely for face matching. The question this raises is whether a face recognition system could be leveraged to solve these other problems and, if so, how effective it could be. In the current work, we explore the limits of such a system for gender and ethnicity identification given (1) a ground truth of demographically-labeled, textureless 3-D models of human faces and (2) a state-of-the-art face-recognition algorithm. Once trained, our system is capable of classifying the gender and ethnicity of any such model of interest. Experiments are conducted on 4007 facial meshes from the benchmark Face Recognition Grand Challenge v2 dataset.

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References

  • Aharon, M., & Kimmel, R. (2006). Representation analysis and synthesis of lip images using dimensionality reduction. International Journal of Computer Vision, 67(3), 297–312.

    Article  Google Scholar 

  • Baluja, S., & Rowley, H. A. (2007). Boosting sex identification performance. International Journal of Computer Vision, 71(1), 111–119.

    Article  Google Scholar 

  • Bronstein, A. M., Bronstein, M. M., & Kimmel, R. (2006). Efficient computation of isometry-invariant distances between surfaces. SIAM Journal on Scientific Computing, 28(5), 1812–1836.

    Article  MATH  MathSciNet  Google Scholar 

  • Bronstein, A. M., Bronstein, M. M., & Kimmel, R. (2007). Expression-invariant representations of faces. IEEE Transactions on Image Processing, 16(1), 188–197.

    Article  MathSciNet  Google Scholar 

  • Chang, C. C., & Lin, C. J. (2001). LIBSVM: A library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm. Accessed August 2009.

  • Elbaz, A. E., & Kimmel, R. (2003). On bending invariant signatures for surfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(10), 1285–1295.

    Article  Google Scholar 

  • Gutta, S., Huang, J. J., Jonathan, P., & Wechsler, H. (2000). Mixture of experts for classification of gender, ethnic origin, and pose of human faces. IEEE Transactions on Neural Networks, 11(4), 948–960.

    Article  Google Scholar 

  • Härdle, W., & Simar, L. (2003). Applied multivariate statistical analysis (1st edn.). Berlin: Springer.

    MATH  Google Scholar 

  • Hosoi, S., Takikawa, E., & Kawade, M. (2004). Ethnicity estimation with facial images. In IEEE int conf automatic face and gesture recognition.

  • Kakadiaris, I. A., Passalis, G., Toderici, G., Murtuza, M. N., Lu, Y., Karampatziakis, N., & Theoharis, T. (2007). Three-dimensional face recognition in the presence of facial expressions: An annotated deformable model approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(4), 640–649.

    Article  Google Scholar 

  • Kruskal, J. B., & Wish, M. (1978). Multidimensional scaling. Thousand Oaks: SAGE Publications.

    Google Scholar 

  • Lian, H. C., Lu, B. L., Takikawa, E., & Hosoi, S. (2005). Gender recognition using a min-max modular support vector machine. In Int conf advances in natural computation (pp. 438–441).

  • Lu, X., & Jain, A. K. (2004). Ethnicity identification from face images. In SPIE int symp defense and security (pp. 114–123).

  • Lu, X., Chen, H., & Jain, A. K. (2006). Multimodal facial gender and ethnicity identification. In Int conf biometrics, Hong Kong.

  • Mäkinen, E., & Raisamo, R. (2008). Evaluation of gender classification methods with automatically detected and aligned faces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(3), 541–547.

    Article  Google Scholar 

  • Moghaddam, B., & Yang, M. (2002). Learning gender with support faces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5), 707–711.

    Article  Google Scholar 

  • O’Toole, A. J., Vetter, T., Bülthoff, H. H., & Troje, N. F. (1995). The role of shape and texture information in sex classification. Tech. rep., Max Planck Institut für biologische Kybernetik.

  • O’Toole, A. J., Vetter, T., Troje, N. F., & Bülthoff, H. H. (1997). Sex classification is better with three-dimensional structure than with image intensity information. Perception, 26, 75–84.

    Article  Google Scholar 

  • Phillips, P. J., Flynn, P. J., Scruggs, T., Bowyer, K. W., Chang, J., Hoffman, K., Marques, J., Min, J., & Worek, W. (2005). Overview of the face recognition grand challenge. In IEEE conf comp vis pattern recogn.

  • Potter, T., Corneille, O., Ruys, K. I., & Rhodes, G. (2007). “Just another pretty face”: A multidimensional scaling approach to face attractiveness and variability. Psychonomic Bulletin & Review, 14(2), 368–372.

    Google Scholar 

  • Seber, G. (1984). Multidimensional scaling, Multivariate observations. New York: Wiley. Chap 5.5.

    Book  MATH  Google Scholar 

  • Shi, J., & Malik, J. (2000). Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 888–905.

    Article  Google Scholar 

  • Simoncelli, E., Freeman, W., Adelson, E., & Heeger, D. (1992). Shiftable multi-scale transforms. IEEE Transactions on Information Theory, 38, 587–607.

    Article  MathSciNet  Google Scholar 

  • Tsogo, L., Masson, M. H., & Bardot, A. (2000). Multidimensional scaling methods for many-object sets: A review. Multivariate Behavioral Research, 35(3), 307–319.

    Article  Google Scholar 

  • Wang, Z., Bovik, A., Sheikh, H., & Simoncelli, E. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–612.

    Article  Google Scholar 

  • Wu, J., Smith, W. A. P., & Hancock, E. R. (2007). Gender classification using shape from shading. In British machine vision conference.

  • Wu, J., Smith, W. A. P., & Hancock, E. R. (2008). Facial gender classification using shape from shading and weighted principal geodesic analysis. In Int conf image anal recogn.

  • Yang, Z., & Ai, H. (2007). Demographic classification with local binary patterns. In Int conf biometrics, Seoul, Korea (pp. 464–473).

  • Young, F. W. (1987). Multidimensional scaling: history, theory, and applications. Hillsdale: Erlbaum.

    Google Scholar 

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Correspondence to George Toderici.

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Toderici, G., O’Malley, S.M., Passalis, G. et al. Ethnicity- and Gender-based Subject Retrieval Using 3-D Face-Recognition Techniques. Int J Comput Vis 89, 382–391 (2010). https://doi.org/10.1007/s11263-009-0300-7

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  • DOI: https://doi.org/10.1007/s11263-009-0300-7

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