Skip to main content
Log in

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

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Ahonen T, Pietikäinen M (2007) Soft histograms for local binary patterns, in proc. Finnish Signal Processing Symposium (FINSIG)

  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):2037–2041

  3. Anila S, Devarajan N (2012) Preprocessing Technique for Face Recognition Applications Under Varying Illumination Conditions. Global Journal of Computer Science and Technology

  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, 1–8

  5. Cao Q, Ying Y, Li P (2013) Similarity metric learning for face recognition. International Conference on Computer Vision (ICCV)

  6. Censor Y, Zenios S (1998) Parallel optimization: theory, algorithms and applications. Oxford University Press, USA

  7. Chang C-C, Lin C-J (2001) LIBSVM: A Library for Support Vector Machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm

  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)

  9. Davis J, Kulis B, Jain P, Sra S, Dhilon I (2007) Information-theoretic metric learning. ICML

  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)

  11. Efron B, Hastie T, Johnstone I, Tibshirani R (2004) Least angle regression. Ann Stat 32(2):407–499

  12. Etemad K, Chellappa R (1997) Discriminant analysis for recognition of human face images. J Opt Soc Am 14:1724–1733

  13. Fan H, Cao Z, Jiang Y, Yin Q, Doudou C (2014) Learning deep face representation. Technical report, Megvii. Inc, Beijing

  14. Guillaumin M, Verbeek J, Schmid C (2009) Is that you? Metric learning approaches for face identification. International Conference on Computer Vision (ICCV)

  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 Recognition

  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 07-49

  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) 437–446

  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 TR115

  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 Reviews

  20. Hussain S, Napolean T, Jurie F (2012) Face recognition using local quantized patterns. British machine vision conference

  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) 306–309

  22. Kan M, Shan S, Xu D, Chen X (2011) Side-information based linear discriminant analysis for face recognition. British Machine Vision Conference (BMVC), UK

  23. Kumar N, Berg AC, Belhumeur PN, Nayar SK (2009) Attribute and simile classifiers for face verification. International Conference on Computer Vision (ICCV)

  24. Li Z, Imai J, Kaneko M (2010) Robust face recognition using block-based bag of words. International Conference on Pattern Recognition

  25. Nguyen HV (2011) Linear Subspace Methods in Face Recognition (Ph.D. thesis), University of Nottingham

  26. Nguyen HV, Bai L (2010) Cosine similarity metric learning for face verification. Asian Conference on Computer Vision (ACCV)

  27. Pollard DE (2002) A user’s guide to measure theoretic probability. Cambridge University Press

  28. Simonyan K, Parkhi OM, Vedaldi A, Zisserman A (2013) Fisher vector faces in the wild. In British Machine Vision Conference

  29. Sun Y, Wang X, Tang X (2013) Hybrid deep learning for face verification. IEEE International Conference on Computer Vision (ICCV)

  30. Sun Y, Wang X, Tang X (2014a) Deep learning face representation from predicting 10,000 classes. IEEE Conference on Computer Vision and Pattern Recognition

  31. Sun Y, Wang X, Tang X (2014b) Deep learning face representation by joint identification-verification, in neural information processing systems (NIPS)

  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 Recognition

  33. Turk M, Pentland A (1991) Eigenfaces for Recognition. J Cogn Neurosci 3(1):71–86

  34. Welling M (2005) Fisher Linear Discriminant Analysis. http://www.ics.uci.edu/~welling/classnotes/papers_class/Fisher-LDA.pdf

  35. Wolf L, Hassner T, Taigman Y (2008) Descriptor based methods in the wild. European Conference on Computer Vision

  36. Wolf L, Hassner T, Taigman Y (2009) Similarity Scores based on Background Samples. Asian Conference on Computer Vision (ACCV)

  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 Intelligence

  38. Yang H, Wang Y (2007) A LBP-based face recognition method with hamming distance constraint, in Proc. Int. Conf. Image and Graphics (ICIG) 645–649

Download references

Acknowledgments

This research is supported by Fundamental Research Grant Scheme (FRGS) of Malaysia under grants MMUE/140026.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Andrew Beng Jin Teoh.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chong, SC., Teoh, A.B.J. & Ong, TS. Unconstrained face verification with a dual-layer block-based metric learning. Multimed Tools Appl 76, 1703–1719 (2017). https://doi.org/10.1007/s11042-015-3120-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-015-3120-5

Keywords

Navigation