Multimedia Tools and Applications

, Volume 75, Issue 19, pp 11831–11846 | Cite as

Ensemble based extreme learning machine for cross-modality face matching

Article
First Online:
Received:
Revised:
Accepted:

Abstract

Extreme learning machine (ELM) is one of the most important and efficient machine learning algorithms for pattern classification due to its fast learning speed. In this paper, we propose a new ensemble based ELM approach for cross-modality face matching. Different to traditional face recognition methods, the proposed approach integrates the voting-base extreme learning machine (V-ELM) with a novel feature learning based face descriptor. Firstly, the discriminant feature learning is proposed to learn the cross-modality feature representation. Then, we used common subspace learning based method to reduce the obtained cross-modality features. Finally, Voting ELM is utilized as the classifier to improve the recognition accuracy and to speed up the feature learning process. Experiments conducted on two different heterogeneous face recognition scenarios demonstrate the effectiveness of our proposed approach.

Keywords

Extreme learning machine Neural network Cross-modality matching Feature learning Canonical correlation analysis 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgments

This work was supported by the fundamental research funds for the central universities (K14JB00230) and the National Natural Science Foundation of China (No. 61403024, 31201358, 61100141).

References

  1. 1.
    Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns:application to face recognition. IEEE Trans Pattern Anal Mach Intell 11(12):2037–2041CrossRefMATHGoogle Scholar
  2. 2.
    Cao J, Lin Z, Huang G-B, Liu N (2012) Voting based extreme learning machine. Inf Sci 185(1):66–77MathSciNetCrossRefGoogle Scholar
  3. 3.
    Cao J, Xiong L (2014) Protein sequence classification with improved extreme learning machine algorithms. BioMed Research International, p 2014Google Scholar
  4. 4.
    Chen J, Yi D, Yang J, Zhao G, Li S, Pietikainen M. (2009) Learning mapping for face synthesis from near infrared to visual light images. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp 156–163Google Scholar
  5. 5.
    Fung G, Mangasarian OL (2001) Proximal support vector machine classifiers. In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp 77–86Google Scholar
  6. 6.
    Gao X, Zhong J, Li J, Tian C (2005) Face sketch synthesis algorithm based on e-hmm and selective ensemble. IEEE Trans. Circuits Syst. Video Technol. 4:487–496Google Scholar
  7. 7.
    Hardoon DR, Szedmak S, Shawe-Taylor J (2004) Canonical correlation analysis: An overview with application to learning method. Neural Comput 16:2639–2664CrossRefMATHGoogle Scholar
  8. 8.
    Hotelling H (1936) Relations between two sets of variates. Biometrika 28:321–377CrossRefMATHGoogle Scholar
  9. 9.
    Huang G-B, Wang DH, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Learn Cybern 2(2):107–122CrossRefGoogle Scholar
  10. 10.
    Huang G-B, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B Cybern 42(2):513–529CrossRefGoogle Scholar
  11. 11.
    Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine theory and applications. Neurocomputing 70(1):489–501CrossRefGoogle Scholar
  12. 12.
    Huang LK, Lu JW, Tan Y-P (2012) Learning modality-invariant features for heterogeneous face recognition. In: Proceedings of IEEE International Conference on Pattern Recognition, pp 1683– 1686Google Scholar
  13. 13.
    Huang XS, Lei Z, Fan MY, Wang X, Li SZ (2013) Regularized discriminative spectral regression method for heterogeneous face matching. IEEE Trans Image Process 22(1):353–362MathSciNetCrossRefGoogle Scholar
  14. 14.
    HuiJuan L, An C, Zheng E, Yi L (2014) Dissimilarity based ensemble of extreme learning machine for gene expression data classification. Neurocomputing 128(0):22–30Google Scholar
  15. 15.
    Kasun LLC, Zhou H, Huang G-B, Vong CM (2013) Representational learning with extreme learning machine for big data. IEEE Intelligent SystemsGoogle Scholar
  16. 16.
    Klare BF, Anil KJ (2013) Heterogeneous face recognition using kernel prototype similarities. IEEE Trans Pattern Anal Mach Intell 6:1410–1422CrossRefGoogle Scholar
  17. 17.
    Klare BF, Jain AK (2013) Heterogeneous face recognition using kernel prototype similarities. IEEE Trans Pattern Anal Mach Intell 35(6):1410–1422CrossRefGoogle Scholar
  18. 18.
    Klare BF, Li Z, Jain AK (2011) Matching forensic sketches to mug shot photos. IEEE Trans Pattern Anal Mach Intell 33(3):639–646CrossRefGoogle Scholar
  19. 19.
    Lei Z, Li SZ (2009) Coupled spectral regressoin for matching heterogeneous faces. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp 1123–1128Google Scholar
  20. 20.
    Lei Z, Liao SC, Jain AK, Li SZ (2012) Coupled discriminant analysis for heterogeneous face recognition. IEEE Trans Inf Forensics Secur 7(6):1707–1716CrossRefGoogle Scholar
  21. 21.
    Lei Z, Pietikainen M, Li SZ (2014) Learning discriminant face descriptor. IEEE Trans Pattern Anal Mach Intell 36(2):289–302CrossRefGoogle Scholar
  22. 22.
    Lei Z, Yi D, Li SZ (2012) Discriminant image filter learning for face recognition with local binary pattern like representation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp 2512–2517Google Scholar
  23. 23.
    Li A, Shan S, Chen X, Gao W (2011) Face recognition based on non-corresponding region matching. In: Proceedings of IEEE International Conference on Computer Vision, pp 1060–1067Google Scholar
  24. 24.
    Li SZ, Lei Z, Ao M (2009) The hfb face database for heterogeneous face biometrics research. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp 1–8Google Scholar
  25. 25.
    Li Z, Gong D, Qiao Y, Tao D (2014) Common feature discriminant analysis for matching infrared face images to optical face images. IEEE Trans Image Process 23 (6):2436–2445MathSciNetCrossRefGoogle Scholar
  26. 26.
    Liao S, Yi D, Lei Z, Qin R, Li S (2009) Heterogeneous face recognition from local structures of normalized appearance. In: Proceedings of International Conference on Biometrics, pp 209– 218Google Scholar
  27. 27.
    Lin D, Tang X (2006) Inter-modality face recognition. In: Proceedings of the European Conference on Computer Vision, pp 13–26Google Scholar
  28. 28.
    Liu N, Wang H (2010) Ensemble based extreme learning machine. IEEE Signal Process Lett 17(8):754–757CrossRefGoogle Scholar
  29. 29.
    Liu Q, Tang X, Jin H, Lu H, Ma S (2005) A nonlinear approach for face sketch synthesis and recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp 1005– 1010Google Scholar
  30. 30.
    Long X, Hongtao L, Peng Y, Li W (2014) Graph regularized discriminative non-negative matrix factorization for face recognition. Multimedia Tools and Applications 72(3):2679–2699CrossRefGoogle Scholar
  31. 31.
    Lowe D (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110CrossRefGoogle Scholar
  32. 32.
    Mohammed AA, Minhas R, Jonathan Wu QM, Sid-Ahmed MA (2011) Human face recognition based on multidimensional pca and extreme learning machine. Pattern Recogn 44(10–11):2588– 2597CrossRefMATHGoogle Scholar
  33. 33.
    Phillips PJ, Flynn P, Scruggs T, Bowyer KW, Chang J, Hoffman K, Marques J, Min J, Worek W (2005) Overview of the face recognition grand challenge. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol 1, pp 947–954Google Scholar
  34. 34.
    Sharma A, Jacobs DW (2011) Bypassing synthesis,Pls for face recognition with pose, low-resolution and sketch. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp 593– 600Google Scholar
  35. 35.
    Serre D (2002) Matrices: theory and applications. SpringerGoogle Scholar
  36. 36.
    Sun Q, Zeng S, Liu Y, Heng PA, Xia DS (2005) A new method of feature fusion and its application in image recognition. Pattern Recogn 38(12):2437–2448CrossRefGoogle Scholar
  37. 37.
    Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300MathSciNetCrossRefMATHGoogle Scholar
  38. 38.
    Tan X, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 19(6):1635–1650MathSciNetCrossRefGoogle Scholar
  39. 39.
    Tang X, Wang X (2004) Face sketch recognition. IEEE Trans Circuits Syst Video Technol 1:50–57CrossRefGoogle Scholar
  40. 40.
    Wang X, Tang X (2009) Face photo-sketch synthesis and recognition. IEEE Trans Pattern Anal Mach Intell 11:1955–1967CrossRefGoogle Scholar
  41. 41.
    Wold H (1975) Quantitative sociology: International perspectives on mathematical and statistical modeling (quantitative studies in social relations), vol 16. Academic press edn. Academic Press, London, pp 307–357CrossRefGoogle Scholar
  42. 42.
    Yang D, Han F (2014) An improved ensemble of extreme learning machine based on attractive and repulsive particle swarm optimization. In: Intelligent Computing Theory, volume 8588 of Lecture Notes in Computer Science, pp 213–220Google Scholar
  43. 43.
    Yi D, Lei Z, Liao S, Li SZ (2014) Shared representation learning for heterogeneous face recognition. arXiv:1406.1247
  44. 44.
    Zhai J-h, Hong-yu X, Wang X-z (2012) Dynamic ensemble extreme learning machine based on sample entropy. Soft Comput 16(9):1493–1502CrossRefGoogle Scholar
  45. 45.
    Zhang P, Zhixin Y (2015) Ensemble extreme learning machine based on a new self-adaptive adaboost.rt,. In: Proceedings of ELM-2014 Volume 1, of Proceedings in Adaptation, Learning and Optimization, vol 3, pp 237–244Google Scholar
  46. 46.
    Zong W, Huang G-B (2011) Face recognition based on extreme learning machine. Neurocomputing 74(16):2541–2551CrossRefGoogle Scholar
  47. 47.
    Zong W, Zhou H, Huang G-B, Lin Z (2011) Face recognition based on kernelized extreme learning machine. In: Autonomous and Intelligent Systems, of Lecture Notes in Computer Science, vol 6752, pp 263–272Google Scholar
  48. 48.
    Zhu J-Y, Zheng W-S, Lai J-H, Li SZ (2014) Matching nir face to vis face using transduction. IEEE Trans Inf Forensics Secur 9(3):501–514CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijingChina
  2. 2.Institute of Information and ControlHangzhou Dianzi UniversityZhejiangChina
  3. 3.China National Institute of StandardizationBeijingChina

Personalised recommendations