Multimedia Tools and Applications

, Volume 76, Issue 2, pp 1703–1719 | Cite as

Unconstrained face verification with a dual-layer block-based metric learning

  • Siew-Chin Chong
  • Andrew Beng Jin Teoh
  • Thian-Song Ong


In this paper, a dual-layer block-based metric learning technique is proposed to better discriminate the face image pairs and accelerate the overall verification process under the unconstrained environment. The input images are processed as blocks to provide a richer base of face features. Our proposed method is formed by two layers, in which the first layer assists in extracting the compact block-based descriptors without the existence of full class label information and to refine the within-class and between-class scatter matrices while the second layer integrates the face descriptors of all blocks. The proposed scheme has computational advantage over the single metric learning method while it exploits the correlations among the multiple metrics from different descriptors. The performance of our proposed method is evaluated on the Labeled Faces in the Wild database and achieves an improved performance when compared with the state-of-the-art methods in terms of verification rate and computation time.


Unconstrained face Metric learning Block-based Verification Restricted 


  1. 1.
    Ahonen T, Pietikäinen M (2007) Soft histograms for local binary patterns, in proc. Finnish Signal Processing Symposium (FINSIG)Google Scholar
  2. 2.
    Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Machine Intell 28(12):2037–2041Google Scholar
  3. 3.
    Anila S, Devarajan N (2012) Preprocessing Technique for Face Recognition Applications Under Varying Illumination Conditions. Global Journal of Computer Science and TechnologyGoogle Scholar
  4. 4.
    Barkan O, Weill J, Wolf L, Aronowitz H (2013) Fast high dimensional vector multiplication face recognition, computer vision (ICCV), 2013 I.E. International Conference on, pp. 1960–1967, 1–8Google Scholar
  5. 5.
    Cao Q, Ying Y, Li P (2013) Similarity metric learning for face recognition. International Conference on Computer Vision (ICCV)Google Scholar
  6. 6.
    Censor Y, Zenios S (1998) Parallel optimization: theory, algorithms and applications. Oxford University Press, USAGoogle Scholar
  7. 7.
    Chang C-C, Lin C-J (2001) LIBSVM: A Library for Support Vector Machines. Software available at
  8. 8.
    Cui Z, Li W, Xu D, Shan S, Chen X (2013) Fusing robust face region descriptors via multiple metric learning for face recognition in the wild. IEEE Conf Comput Vision Pattern Recogn (CVPR)Google Scholar
  9. 9.
    Davis J, Kulis B, Jain P, Sra S, Dhilon I (2007) Information-theoretic metric learning. ICMLGoogle Scholar
  10. 10.
    Chen D, Cao X, Wen F, Sun J (2013) Blessing of dimensionality: high-dimensional feature and its efficient compression for face verification. Comput Vision Pattern Recogn (CVPR)Google Scholar
  11. 11.
    Efron B, Hastie T, Johnstone I, Tibshirani R (2004) Least angle regression. Ann Stat 32(2):407–499Google Scholar
  12. 12.
    Etemad K, Chellappa R (1997) Discriminant analysis for recognition of human face images. J Opt Soc Am 14:1724–1733Google Scholar
  13. 13.
    Fan H, Cao Z, Jiang Y, Yin Q, Doudou C (2014) Learning deep face representation. Technical report, Megvii. Inc, BeijingGoogle Scholar
  14. 14.
    Guillaumin M, Verbeek J, Schmid C (2009) Is that you? Metric learning approaches for face identification. International Conference on Computer Vision (ICCV)Google Scholar
  15. 15.
    Hu J, Lu J, Tan Y-P (2014) Discriminative Deep Metric Learning for Face Verification in the Wild. IEEE Conference on Computer Vision and Pattern RecognitionGoogle Scholar
  16. 16.
    Huang GB, Ramesh M, Berg T, Learned-Miller E (2007a) Labeled faces in the wild: a database for studying face recognition in unconstrained environments. University ofMassachusetts, Amherst, Technical Report 07-49Google Scholar
  17. 17.
    Huang D, Wang Y, Wang Y (2007b) A robust method for near infrared face recognition based on extended local binary pattern, in Proc. Int. Symposium on Visual Computing (ISVC) 437–446Google Scholar
  18. 18.
    Huang C, Zhu S, Yu K (2011a) Large scale strongly supervised ensemble metric learning, with applications to face verification and retrieval. NEC Technical Report TR115Google Scholar
  19. 19.
    Huang D, Shan C, Ardabilian M, Wang Y, Chen L (2011b) Local binary patterns and its application to facial image analysis: a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and ReviewsGoogle Scholar
  20. 20.
    Hussain S, Napolean T, Jurie F (2012) Face recognition using local quantized patterns. British machine vision conferenceGoogle Scholar
  21. 21.
    Jin H, Liu Q, Lu H, Tong X (2004) Face detection using improved LBP under Bayesian Framework, in Proc Int. Conf. Image and Graphics (ICIG) 306–309Google Scholar
  22. 22.
    Kan M, Shan S, Xu D, Chen X (2011) Side-information based linear discriminant analysis for face recognition. British Machine Vision Conference (BMVC), UKGoogle Scholar
  23. 23.
    Kumar N, Berg AC, Belhumeur PN, Nayar SK (2009) Attribute and simile classifiers for face verification. International Conference on Computer Vision (ICCV)Google Scholar
  24. 24.
    Li Z, Imai J, Kaneko M (2010) Robust face recognition using block-based bag of words. International Conference on Pattern RecognitionGoogle Scholar
  25. 25.
    Nguyen HV (2011) Linear Subspace Methods in Face Recognition (Ph.D. thesis), University of NottinghamGoogle Scholar
  26. 26.
    Nguyen HV, Bai L (2010) Cosine similarity metric learning for face verification. Asian Conference on Computer Vision (ACCV)Google Scholar
  27. 27.
    Pollard DE (2002) A user’s guide to measure theoretic probability. Cambridge University PressGoogle Scholar
  28. 28.
    Simonyan K, Parkhi OM, Vedaldi A, Zisserman A (2013) Fisher vector faces in the wild. In British Machine Vision ConferenceGoogle Scholar
  29. 29.
    Sun Y, Wang X, Tang X (2013) Hybrid deep learning for face verification. IEEE International Conference on Computer Vision (ICCV)Google Scholar
  30. 30.
    Sun Y, Wang X, Tang X (2014a) Deep learning face representation from predicting 10,000 classes. IEEE Conference on Computer Vision and Pattern RecognitionGoogle Scholar
  31. 31.
    Sun Y, Wang X, Tang X (2014b) Deep learning face representation by joint identification-verification, in neural information processing systems (NIPS)Google Scholar
  32. 32.
    Taigman Y, Yang M, Ranzato MA, Wolf L (2014) DeepFace: closing the gap to human-level performance in face verification. IEEE Conference on Computer Vision and Pattern RecognitionGoogle Scholar
  33. 33.
    Turk M, Pentland A (1991) Eigenfaces for Recognition. J Cogn Neurosci 3(1):71–86Google Scholar
  34. 34.
    Welling M (2005) Fisher Linear Discriminant Analysis. Scholar
  35. 35.
    Wolf L, Hassner T, Taigman Y (2008) Descriptor based methods in the wild. European Conference on Computer VisionGoogle Scholar
  36. 36.
    Wolf L, Hassner T, Taigman Y (2009) Similarity Scores based on Background Samples. Asian Conference on Computer Vision (ACCV)Google Scholar
  37. 37.
    Wolf L, Hassner T, Taigman Y (2011) Effective unconstrained face recognition by combining multiple descriptors and learned background statistics. IEEE Transactions On Pattern Analysis and Machine IntelligenceGoogle Scholar
  38. 38.
    Yang H, Wang Y (2007) A LBP-based face recognition method with hamming distance constraint, in Proc. Int. Conf. Image and Graphics (ICIG) 645–649Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Siew-Chin Chong
    • 1
  • Andrew Beng Jin Teoh
    • 2
  • Thian-Song Ong
    • 1
  1. 1.Faculty of Information Science and TechnologyMultimedia UniversityMelakaMalaysia
  2. 2.School of Electrical and Electronic EngineeringYonsei UniversitySeoulSouth Korea

Personalised recommendations