Neural Computing and Applications

, Volume 24, Issue 6, pp 1341–1353 | Cite as

Distance approximation for two-phase test sample representation in face recognition

Original Article

Abstract

The two-phase test sample representation (TPTSR) scheme was proposed as a useful method for face recognition; however, the sample selection based on sparse representation in the first phase is not necessary. This is because the first phase only plays a role of course search in TPTSR, but the sparse representation method is suitable for fine classification. This paper proves that alternative nearest-neighbor selection criterions with higher efficiency can be used in the first phase of TPTSR without compromising the classification accuracy. Theoretical analysis and experimental results show that the original distance metric based on sparse representation in the first phase of the TPTSR can be approximated with a more straightforward metric while maintaining a comparable classification performance with the original TPTSR. Therefore, the computational load of the TPTSR can be greatly reduced.

Keywords

Computer vision Face recognition Pattern recognition Sparse representation Transform methods 

References

  1. 1.
    Kirby M, Sirovich L (1990) Application of the KL phase for the characterization of human faces. IEEE Trans Pattern Anal Mach Intell 12:103–108CrossRefGoogle Scholar
  2. 2.
    Xu Y, Zhang D, Yang J, Yang J-Y (2008) An approach for directly extracting features from matrix data and its application in face recognition. Neurocomputing 71:1857–1865CrossRefGoogle Scholar
  3. 3.
    Yang J, Zhang D, Frangi AF, Yang J-Y (2004) Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans Pattern Anal Mach Intell 26:131–137CrossRefGoogle Scholar
  4. 4.
    Xu Y, Zhang D (2010) Represent and fuse bimodal biometric images at the feature level: complex-matrix-based fusion scheme. Opt Eng 49(3):037002Google Scholar
  5. 5.
    Park SW, Savvides M (2010) A multifactor extension of linear discriminant analysis for face recognition under varying pose and illumination. EURASIP J Adv Signal Process 2010:11CrossRefGoogle Scholar
  6. 6.
    Fan Z, Xu Y, Zhang D (2011) Local linear discriminant analysis framework using sample neighbors. IEEE Trans Neural Netw 22:1119–1132CrossRefGoogle Scholar
  7. 7.
    Sugiyama M (2007) Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis. J Mach Learn Res 8:1027–1061MATHGoogle Scholar
  8. 8.
    Vural V, Fung G, Krishnapuram B, Dy JG, Rao B (2009) Using local dependencies within batches to improve large margin classifiers. J Mach Learn Res 10:183–206MATHGoogle Scholar
  9. 9.
    Liu ZY, Chiu KC, Xu L (2003) Improved system for object detection and star/galaxy classification via local subspace analysis. Neural Netw 16(3–4):437–451CrossRefGoogle Scholar
  10. 10.
    Yang Y, Xu D, Nie F, Yan S, Zhuang Y (2010) Image clustering using local discriminant models and global integration. IEEE Trans Image Process 19(10):2761–2773CrossRefMathSciNetGoogle Scholar
  11. 11.
    Lai Z, Jin Z, Yang J, Wong WK (2010) Sparse local discriminant projections for feature extraction. In: ICPR, pp 926–929Google Scholar
  12. 12.
    Wright J, Ma Y, Mairal J, Sapiro G, Huang TS, Yan S (2010) Sparse representation for computer vision and pattern recognition. In: IEEE transactions on neural network, pp 1031–1044Google Scholar
  13. 13.
    Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31:210–227CrossRefGoogle Scholar
  14. 14.
    Mairal J, Bach F, Ponce J, Sapiro G, Zisserman A (2009) Supervised dictionary learning. In: Advances in neural information processing systems (NIPS)Google Scholar
  15. 15.
    Shi Y, Dai DQ, Liu CC, Yan H (2009) Sparse discriminant analysis for breast cancer biomarker identification and classification. Prog Nat Sci 19:1635–1641CrossRefGoogle Scholar
  16. 16.
    Dikmen M, Huang T (2008) Robust estimation of foreground in surveillance videos by sparse error estimation. In: International conference on pattern recognitionGoogle Scholar
  17. 17.
    Elhamifar E, Vidal R (2009) Sparse subspace clustering. In: IEEE international conference on computer vision and pattern recognition, pp. 2790–2797Google Scholar
  18. 18.
    Rao S, Tron R, Vidal R, Ma Y (2008) Motion segmentation via robust subspace separation in the presence of outlying, incomplete, and corrupted trajectories. In: IEEE international conference on computer vision and pattern recognition, pp 1–8Google Scholar
  19. 19.
    Zhang L, Yang M, Feng X (2011) Sparse representation or collaborative representation: which helps face recognition? In: ICCVGoogle Scholar
  20. 20.
    Casasent D (1984) Unified synthetic discriminant function computational formulation. Appl Opt 23:1620–1627CrossRefGoogle Scholar
  21. 21.
    Li SZ (1998) Face recognition based on nearest linear combinations. In: Proceedings of IEEE international conference on computer vision and pattern recognition, pp 839–844Google Scholar
  22. 22.
    Li SZ, Lu J (1999) Face recognition using nearest feature line method. IEEE Trans Neural Netw 10:439–443CrossRefGoogle Scholar
  23. 23.
    Xu Y, Zhang D, Yang J, Yang J-Y (2011) A two-phase test sample sparse representation method for use with face recognition. In: IEEE Transactions on Circuits and Systems for Video Technology, vol 21Google Scholar
  24. 24.
    Zhang L (2011) Sparse representation or collaborative representation: which helps face recognition? In: ICCVGoogle Scholar
  25. 25.
    Breu H, Gil J, Kirkpatrick D, Werman M (1995) Linear time Euclidean distance transform algorithms. IEEE Trans Pattern Anal Mach Intell 17:529–533CrossRefGoogle Scholar
  26. 26.
    Krause EF (1987) Taxicab geometry. Dover, NYGoogle Scholar
  27. 27.
    Spearman C (1904) The proof and measurement of association between two things. Am J Psychol 15:72–101CrossRefGoogle Scholar
  28. 28.
    Minkowski H (1953) Geometrie der Zahlen. Chelsea, New YorkMATHGoogle Scholar
  29. 29.
  30. 30.
  31. 31.
    Phillips PJ, Moon H, Rizvi SA, Rauss PJ (2000) The FERET evaluation methodology for face-recognition algorithms. IEEE Trans Pattern Anal Mach Intell 22:1090–1104CrossRefGoogle Scholar
  32. 32.
    Phillips PJ The facial recognition technology (FERET) database. Available: http://www.itl.nist.gov/iad/humanid/feret/feret-master.html
  33. 33.
    Tumanski S (2006) Principles of electrical measurement. Taylor & Francis Group, New YorkCrossRefGoogle Scholar
  34. 34.
    Xu Y, Jin Z (2008) Down-sampling face images and low-resolution face recognition. In: 3rd International conference on innovative computing, information and control, pp 392–395Google Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.School of Mechanical and Electrical EngineeringHarbin Institute of TechnologyHarbinChina
  2. 2.Shenzhen Key Lab of Wind Power and Smart GridHarbin Institute of Technology Shenzhen Graduate SchoolShenzhenChina

Personalised recommendations