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Facial Ethnic Appearance Synthesis

  • Felix Juefei-XuEmail author
  • Marios Savvides
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8926)

Abstract

In this work, we have explored several subspace reconstruction methods for facial ethnic appearance synthesis (FEAS). In our experiments, our proposed dual subspace modeling using the Fukunaga Koontz transform (FKT) yields much better facial ethnic synthesis results than the \(\ell _1\) minimization, the \(\ell _2\) minimization and the principal component analysis (PCA) reconstruction method. With that, we are able to automatically and efficiently synthesize different facial ethnic appearance and alter the facial ethnic appearance of the query image to any other ethnic appearance as desired. Our technique well preserves the facial structure of the query image and simultaneously synthesize the skin tone and ethnic features that best matches target ethnicity group. Facial ethnic appearance synthesis can be applied to synthesizing facial images of a particular ethnicity group for unbalanced database, and can be used to train ethnicity invariant classifiers by generating multiple ethnic appearances of the same subject in the training stage.

Keywords

Soft biometrics Ethnicity Face synthesis Fukunaga Koontz transform 

References

  1. 1.
    Dhamecha, T.I., Sankaran, A., Singh, R., Vatsa, M.: Is gender classification across ethnicity feasible using discriminant functions? In: 2011 International Joint Conference on Biometrics (IJCB), pp. 1–7 (September 2011)Google Scholar
  2. 2.
    Ekanadham, C., Tranchina, D., Simoncelli, E.P.: Recovery of sparse translation-invariant signals with continuous basis pursuit. IEEE Transactions on Signal Processing 59(10), 4735–4744 (2011)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Fukunaga, K., Koontz, W.L.G.: Application of the karhunen-loève expansion to feature selection and ordering. IEEE Transactions on Computers C-19(4), 311–318 (1970)Google Scholar
  4. 4.
    Gill, P.R., Wang, A., Molnar, A.: The in-crowd algorithm for fast basis pursuit denoising. IEEE Transactions on Signal Processing 59(10), 4595–4605 (2011)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Guo, G., Mu, G.: A study of large-scale ethnicity estimation with gender and age variations. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshop (CVPRW), pp. 79–86 (June 2010)Google Scholar
  6. 6.
    Hoang, T.V., Smith, E.H.B., Tabbone, S.: Edge noise removal in bilevel graphical document images using sparse representation. In: 2011 18th IEEE International Conference on Image Processing (ICIP), pp. 3549–3552 (September 2011)Google Scholar
  7. 7.
    Hosoi, S., Takikawa, E., Kawade, M.: Ethnicity estimation with facial images. In: Sixth IEEE International Conference on Automatic Face and Gesture Recognition (FG), pp. 195–200 (May 2004)Google Scholar
  8. 8.
    Jain, A.K., Park, U.: Facial marks: Soft biometric for face recognition. In: 2009 16th IEEE International Conference on Image Processing (ICIP), pp. 37–40 (November 2009)Google Scholar
  9. 9.
    Juefei-Xu, F., Bhagavatula, C., Jaech, A., Prasad, U., Savvides, M.: Gait-id on the move: pace independent human identification using cell phone accelerometer dynamics. In: 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 8–15. IEEE (2012)Google Scholar
  10. 10.
    Juefei-Xu, F., Cha, M., Heyman, J.L., Venugopalan, S., Abiantun, R., Savvides, M.: Robust local binary pattern feature sets for periocular biometric identification. In: 2010 Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), pp. 1–8. IEEE (2010)Google Scholar
  11. 11.
    Juefei-Xu, F., Cha, M., Savvides, M., Bedros, S., Trojanova, J.: Robust periocular biometric recognition using multi-level fusion of various local feature extraction techniques. In: IEEE 17th International Conference on Digital Signal Processing (DSP) (2011)Google Scholar
  12. 12.
    Juefei-Xu, F., Luu, K., Savvides, M., Bui, T.D., Suen, C.Y.: Investigating age invariant face recognition based on periocular biometrics. In: 2011 International Joint Conference on Biometrics (IJCB), pp. 1–7. IEEE (2011)Google Scholar
  13. 13.
    Juefei-Xu, F., Pal, D.K., Savvides, M.: Hallucinating the full face from the periocular region via dimensionally weighted K-SVD. In: IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPRW). IEEE (2014)Google Scholar
  14. 14.
    Juefei-Xu, F., Savvides, M.: Can your eyebrows tell me who you are? In: 2011 5th International Conference on Signal Processing and Communication Systems (ICSPCS), pp. 1–8. IEEE (2011)Google Scholar
  15. 15.
    Juefei-Xu, F., Savvides, M.: Unconstrained periocular biometric acquisition and recognition using COTS PTZ camera for uncooperative and non-cooperative subjects. In: 2012 IEEE Workshop on Applications of Computer Vision (WACV), pp. 201–208. IEEE (2012)Google Scholar
  16. 16.
    Juefei-Xu, F., Savvides, M.: An augmented linear discriminant analysis approach for identifying identical twins with the aid of facial asymmetry features. In: IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPRW) (2013)Google Scholar
  17. 17.
    Juefei-Xu, F., Savvides, M.: An image statistics approach towards efficient and robust refinement for landmarks on facial boundary. In: 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 1–8. IEEE (2013)Google Scholar
  18. 18.
    Juefei-Xu, F., Savvides, M.: Subspace based discrete transform encoded local binary patterns representations for robust periocular matching on NIST’s face recognition grand challenge. IEEE Transactions on Image Processing 23(8), 3490–3505 (2014)CrossRefMathSciNetGoogle Scholar
  19. 19.
    Juefei-Xu, F., Savvides, M.: Weight-optimal local binary patterns. In: European Conference on Computer Vision (ECCV) Workshops. Springer (2014)Google Scholar
  20. 20.
    Kashyap, A.L., Tulyakov, S., Govindaraju, V.: Facial behavior as a soft biometric. In: 2012 5th IAPR International Conference on Biometrics (ICB), pp. 147–151 (April 2012)Google Scholar
  21. 21.
    Li, Y., Savvides, M.: Kernel fukunaga-koontz transform subspaces for enhanced face recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8 (June 2007)Google Scholar
  22. 22.
    Liu, C., Zakharov, Y.V., Chen, T.: Broadband underwater localization of multiple sources using basis pursuit de-noising. IEEE Transactions on Signal Processing 60(4), 1708–1717 (2012)CrossRefMathSciNetGoogle Scholar
  23. 23.
    Lu, W., Vaswani, N.: Modified basis pursuit denoising (modified-BPDN) for noisy compressive sensing with partially known support. In: 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 3926–3929 (March 2010)Google Scholar
  24. 24.
    Lu, W., Vaswani, N.: Exact reconstruction conditions for regularized modified basis pursuit. IEEE Transactions on Signal Processing 60(5), 2634–2640 (2012)CrossRefMathSciNetGoogle Scholar
  25. 25.
    Lyle, J.R., Miller, P.E., Pundlik, S.J., Woodard, D.L.: Soft biometric classification using periocular region features. In: 2010 Fourth IEEE International Conference on Biometrics: Theory Application and Systems (BTAS), pp. 1–7 (September 2010)Google Scholar
  26. 26.
    Manesh, F.S., Ghahramani, M., Tan, Y.P.: Facial part displacement effect on template-based gender and ethnicity classification. In: 2010 11th International Conference on Control Automation Robotics and Vision (ICARCV), pp. 1644–1649 (December 2010)Google Scholar
  27. 27.
    Mota, J.F.C., Xavier, J.M.F., Aguiar, P.M.Q., Puschel, M.: Distributed basis pursuit. IEEE Transactions on Signal Processing 60(4), 1942–1956 (2012)Google Scholar
  28. 28.
    Niinuma, K., Park, U., Jain, A.K.: Soft biometric traits for continuous user authentication. IEEE Transactions on Information Forensics and Security 5(4), 771–780 (2010)Google Scholar
  29. 29.
    Park, U., Jain, A.K.: Face matching and retrieval using soft biometrics. IEEE Transactions on Information Forensics and Security 5(3), 406–415 (2010)Google Scholar
  30. 30.
    Reid, D.A., Nixon, M.S.: Using comparative human descriptions for soft biometrics. In: 2011 International Joint Conference on Biometrics (IJCB), pp. 1–6 (October 2011)Google Scholar
  31. 31.
    Savvides, M., Juefei-Xu, F.: Image matching using subspace-based discrete transform encoded local binary patterns (September 2013). http://www.google.com/patents/US20140212044
  32. 32.
    Seshadri, K., Savvides, M.: An analysis of the sensitivity of active shape models to initialization when applied to automatic facial landmarking. IEEE Transactions on Information Forensics and Security 7(4), 1255–1269 (2012)CrossRefGoogle Scholar
  33. 33.
    Wu, B., Ai, H., Huang, C.: Facial image retrieval based on demographic classification. In: 17th International Conference on Pattern Recognition (ICPR), vol. 3, pp. 914–917 (August 2004)Google Scholar
  34. 34.
    Yan, S., Wang, H., Tang, X., Liu, J., Huang, T.S.: Regression from uncertain labels and its applications to soft biometrics. IEEE Trans. on Information Forensics and Security 3(4), 698–708 (2008)Google Scholar
  35. 35.
    Zhang, G., Wang, Y.: Multimodal 2D and 3D facial ethnicity classification. In: 2009 Fifth International Conference on Image and Graphics (ICIG), pp. 928–932 (September 2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Carnegie Mellon UniversityPittsburghUSA

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