Advertisement

International Journal of Computer Vision

, Volume 109, Issue 1–2, pp 94–109 | Cite as

Domain Adaptation for Face Recognition: Targetize Source Domain Bridged by Common Subspace

  • Meina Kan
  • Junting Wu
  • Shiguang ShanEmail author
  • Xilin Chen
Article

Abstract

In many applications, a face recognition model learned on a source domain but applied to a novel target domain degenerates even significantly due to the mismatch between the two domains. Aiming at learning a better face recognition model for the target domain, this paper proposes a simple but effective domain adaptation approach that transfers the supervision knowledge from a labeled source domain to the unlabeled target domain. Our basic idea is to convert the source domain images to target domain (termed as targetize the source domain hereinafter), and at the same time keep its supervision information. For this purpose, each source domain image is simply represented as a linear combination of sparse target domain neighbors in the image space, with the combination coefficients however learnt in a common subspace. The principle behind this strategy is that, the common knowledge is only favorable for accurate cross-domain reconstruction, but for the classification in the target domain, the specific knowledge of the target domain is also essential and thus should be mostly preserved (through targetization in the image space in this work). To discover the common knowledge, specifically, a common subspace is learnt, in which the structures of both domains are preserved and meanwhile the disparity of source and target domains is reduced. The proposed method is extensively evaluated under three face recognition scenarios, i.e., domain adaptation across view angle, domain adaptation across ethnicity and domain adaptation across imaging condition. The experimental results illustrate the superiority of our method over those competitive ones.

Keywords

Face recognition Domain adaptation Common subspace learning Targetize the sourece domain 

Notes

Acknowledgments

This work is partially supported by Natural Science Foundation of China under contracts nos. 61025010, 61173065, and 61222211. The authors would like to thank the guest editors and the reviewers for their valuable comments and suggestions. The authors also would like to thank the Edwin Zinan Zeng for his advices about the writing.

References

  1. Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997). Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 19(7), 711–720.CrossRefGoogle Scholar
  2. Ben-David, S., Blitzer, J., Crammer, K., & Pereira, F. (2007). Analysis of representations for domain adaptation. Advances in Neural Information Processing Systems NIPS, 19, 137–144.Google Scholar
  3. Bickel, S., Brückner, M., & Scheffer, T. (2009). Discriminative learning under covariate shift. The Journal of Machine Learning Research (JMLR), 10, 2137–2155.zbMATHGoogle Scholar
  4. Blitzer, J., McDonald, R., & Pereira, F. (2006). Domain adaptation with structural correspondence learning. In Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 120–128).Google Scholar
  5. Bruzzone, L., & Marconcini, M. (2010). Domain adaptation problems: a dasvm classification technique and a circular validation strategy. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 32(5), 770–787.CrossRefGoogle Scholar
  6. Chen, Y., Wang, G., & Dong, S. (2003). Learning with progressive transductive support vector machine. Pattern Recognition Letters (PRL), 24(12), 1845–1855.CrossRefGoogle Scholar
  7. Donoho, D. L. (2006). For most large underdetermined systems of linear equations the minimal l\(_{1}\)-norm solution is also the sparsest solution. Communications on Pure and Applied Mathematics, 59(6), 797–829.CrossRefzbMATHMathSciNetGoogle Scholar
  8. Duan, L., Tsang, I. W., Xu, D., & Maybank, S. J. (2009). Domain transfer svm for video concept detection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1375–1381).Google Scholar
  9. Duan, L., Xu, D., Tsang, I., & Luo, J. (2012). Visual event recognition in videos by learning from web data. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 34(9), 1667–1680.CrossRefGoogle Scholar
  10. Dudık, M., Schapire, R. E., & Phillips, S. J. (2005). Correcting sample selection bias in maximum entropy density estimation. Advances in Neural Information Processing Systems (NIPS), 17, 323–330.Google Scholar
  11. Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004). Least angle regression. Annals of Statistics, 39(4), 407–499.MathSciNetGoogle Scholar
  12. Gao, X., Wang, X., Li, X., & Tao, D. (2011). Transfer latent variable model based on divergence analysis. Pattern Recognition (PR), 44(10–11), 2358–2366.CrossRefzbMATHGoogle Scholar
  13. Geng, B., Tao, D., & Xu, C. (2011). Daml: Domain adaptation metric learning. IEEE Transactions on Image Processing (T-IP), 20(10), 2980–2989.CrossRefMathSciNetGoogle Scholar
  14. Gong, B., Shi, Y., Sha, F., & Grauman, K. (2012). Geodesic flow kernel for unsupervised domain adaptation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 0, 2066–2073.Google Scholar
  15. Gopalan, R., Li, R., & Chellappa, R. (2011). Domain adaptation for object recognition: An unsupervised approach. In IEEE International Conference on Computer Vision (ICCV) (pp. 999–1006).Google Scholar
  16. Gretton, A., Smola, A., Huang, J., Schmittfull, M., Borgwardt, K., & Schölkopf, B. (2009). Covariate shift by kernel mean matching. Dataset shift in machine learning (pp. 131–160). Cambridge: MIT Press.Google Scholar
  17. Gross, R., Matthews, I., Cohn, J., kanada, T., & Baker, S. (2007). The cmu multi-pose, illumination, and expression (multi-pie) face database. Tech. rep., Carnegie Mellon University Robotics Institute. TR-07-08.Google Scholar
  18. Hal, DI. (2009). Bayesian multitask learning with latent hierarchies. In Conference on Uncertainty in Artificial Intelligence (UAI) (pp. 135–142).Google Scholar
  19. He, X., & Niyogi, P. (2004). Locality preserving projections. Advances in Neural Information Processing Systems NIPS, 16, 153–160.Google Scholar
  20. Huang, J., Smola, A. J., Gretton, A., Borgwardt, K. M., & Schölkopf, B. (2006). Correcting sample selection bias by unlabeled data. In Advances in Neural Information Processing Systems (NIPS).Google Scholar
  21. Huang, K., & Aviyente, S. (2007). Sparse representation for signal classification. Advances in Neural Information Processing Systems NIPS, 19, 609–616.Google Scholar
  22. Jhuo, IH., Liu, D., Lee, D. T., & Chang, S. F. (2012). Robust visual domain adaptation with low-rank reconstruction. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 2168–2175).Google Scholar
  23. Jia, Y., Nie, F., & Zhang, C. (2009). Trace ratio problem revisited. IEEE Transactions on Neural Networks (T-NN), 20(4), 729–735.CrossRefGoogle Scholar
  24. Liu, C., & Wechsler, H. (2002). Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Transactions on Image Processing (T-IP), 11(4), 467–476.CrossRefGoogle Scholar
  25. Mehrotra, R., Agrawal, R., Haider, S. A. (2012). Dictionary based sparse representation for domain adaptation. In ACM International Conference on Information and Knowledge Management (CIKM) (pp. 2395–2398).Google Scholar
  26. Messer, K., Matas, M., Kittler, J., Lttin, J., & Maitre, G. (1999). Xm2vtsdb: The extended m2vts database. In International Conference on Audio and Video-based Biometric Person Authentication (AVBPA) (pp. 72–77).Google Scholar
  27. Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering (T-KDE), 22(10), 1345–1359.CrossRefGoogle Scholar
  28. Pan, S. J., Kwok, J. T., & Yang, Q. (2008) Transfer learning via dimensionality reduction. In AAAI Conference on Artificial Intelligence (AAAI) (pp. 677–682).Google Scholar
  29. Pan, S. J., Tsang, I. W., Kwok, J. T., Yang, Q. (2009). Domain adaptation via transfer component analysis. In International Joint Conferences on Artificial Intelligence (IJCAI) (pp. 1187–1192).Google Scholar
  30. Pan, S. J., Tsang, I. W., Kwok, J. T., & Yang, Q. (2011). Domain adaptation via transfer component analysis. IEEE Transactions on Neural Networks (T-NN), 22(2), 199–210.CrossRefGoogle Scholar
  31. Phillips, P. J., Flynn, P. J., Scruggs, T., Bowyer, K. W., Chang, J., Hoffman, K., et al. (2005). Overview of the face recognition grand challenge. IEEE Conference on Computer Vision and Pattern Recognition CVPR, 1, 947–954.Google Scholar
  32. Qiu, Q., Patel, V. M., Turaga, P., & Chellappa, R. (2012). Domain adaptive dictionary learning. In European Conference on Computer Vision (ECCV) (pp. 631–645).Google Scholar
  33. Raina, R., Battle, A., Lee, H., Packer, B., Ng, A. Y. (2007). Self-taught learning: transfer learning from unlabeled data. In International Conference on Machine Learning (ICML) (pp 759–766).Google Scholar
  34. Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500), 2323–2326.Google Scholar
  35. Shao, M., Castillo, C., Gu, Z., Fu, Y. (2012). Low-rank transfer subspace learning. In IEEE International Conference on Data Mining (ICDM) (pp. 1104–1109).Google Scholar
  36. Shi, Y., & Sha, F. (2012). Information-theoretical learning of discriminative clusters for unsupervised domain adaptation. In International Conference on Machine Learning (ICML). Google Scholar
  37. Shimodaira, Hidetoshi. (2000). Improving predictive inference under covariate shift by weighting the log-likelihood function. Journal of Statistical Planning and Inference, 90(2), 227–244.Google Scholar
  38. Si, S., Tao, D., & Geng, B. (2010). Bregman divergence-based regularization for transfer subspace learning. IEEE Transactions on Knowledge and Data Engineering T-KDE, 22(7), 929–942.CrossRefGoogle Scholar
  39. Si, S., Liu, W., Tao, D., & Chan, K. P. (2011). Distribution calibration in riemannian symmetric space. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 41(4), 921–930.CrossRefGoogle Scholar
  40. Su, Y., Shan, S., Chen, X., & Gao, W. (2009). Hierarchical ensemble of global and local classifiers for face recognition. IEEE Transactions on Image Processing T-IP, 18(8), 1885–1896.CrossRefMathSciNetGoogle Scholar
  41. Sugiyama, M., Nakajima, S., Kashima, H., Buenau, P. V., & Kawanabe, M. (2008). Direct importance estimation with model selection and its application to covariate shift adaptation. In: Advances in Neural Information Processing Systems NIPS, 20, 1433–1440.Google Scholar
  42. Sugiyamai, M., Krauledat, M., & Müller, K. R. (2007). Covariate shift adaptation by importance weighted cross validation. The Journal of Machine Learning Research (JMLR), 8, 985–1005.Google Scholar
  43. Turk, M. A., & Pentland, A. P. (1991). Face recognition using eigenfaces. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 591, 586–591.Google Scholar
  44. Uribe, D. (2010). Domain adaptation in sentiment classification. In International Conference on Machine Learning and Applications (ICMLA) (pp. 857–860).Google Scholar
  45. Wang, H., Yan, S., Xu, D., Tang, X., & Huang, T. (2007). Trace ratio vs. ratio trace for dimensionality reduction. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1–8).Google Scholar
  46. Wang, Z., Song, Y., Zhang, C. (2008). Transferred dimensionality reduction. In European Conference on Principles of Data Mining and Knowledge Discovery (PKDD) (pp. 550–565).Google Scholar
  47. Wright, J., Yang, A. Y., Ganesh, A., Sastry, S. S., & Ma, Y. (2009). Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 31(2), 210–227.CrossRefGoogle Scholar
  48. Xue, Y., Liao, X., Carin, L., & Krishnapuram, B. (2007). Multi-task learning for classification with dirichlet process priors. The Journal of Machine Learning Research (JMLR), 8, 35–63.zbMATHMathSciNetGoogle Scholar
  49. Zadrozny, & Bianca (2004). Learning and evaluating classifiers under sample selection bias. In Proceedings of International Conference on Machine Learning (ICML) (p. 114).Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Meina Kan
    • 1
  • Junting Wu
    • 1
  • Shiguang Shan
    • 1
    Email author
  • Xilin Chen
    • 1
  1. 1.Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology (ICT)CASBeijingChina

Personalised recommendations