Skip to main content
SpringerLink
Log in

Face recognition via fast dense correspondence

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

Abstract

Face recognition plays a significant role in computer vision. It is well know that facial images are complex stimuli signals that suffer from non-rigid deformations, including misalignment, orientation, pose changes, and variations of facial expression, etc. In order to address these variations, this paper introduces an improved sparse-representation based face recognition method, which constructs dense pixel correspondences between training and testing facial samples. Specifically, we first construct a deformable spatial pyramid graph model that simultaneously regularizes matching consistency at multiple spatial extents - ranging from an entire image, though coarse grid cells, to every single pixel. Secondly, a matching energy function is designed to perform face alignment based on dense pixel correspondence, which is very effective to address the issue of non-rigid deformations. Finally, a novel coarse-to-fine matching scheme is designed so that we are able to speed up the optimization of the matching energy function. After the training samples are aligned with respect to testing samples, an improved sparse representation model is employed to perform face recognition. The experimental results demonstrate the superiority of the proposed method over other methods on ORL, AR, and LFWCrop datasets. Especially, the proposed approach improves nearly 4.4 % in terms of recognition accuracy and runs nearly 10 times faster than previous sparse approximation methods.

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

Access this article

Log in via an institution

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
Fig. 7

Similar content being viewed by others

References

  1. Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041

    Article  MATH  Google Scholar 

  2. Assaleh K et al (2014) Combined features for face recognition in surveillance conditions. In: Proceedings of international conference on neural information processing, pp 503–514

  3. Bartlett MS, Movellan JR, Sejnowski TJ (2002) Face recognition by independent component analysis. IEEE Trans Neural Netw 13(6):1450–1464

    Article  Google Scholar 

  4. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

  5. Bin G, Sheng VS, Li S (2015) Bi-parameter space partition for cost-sensitive SVM. In: Proceedings of the 24th international conference on artificial intelligence, pp 3532–3539

  6. Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  7. Bruhn A, Joachim W, Christoph S (2005) Lucas/kanade meets horn/schunck: combining local and global optic flow methods. Int J Comput Vis 61(3):211–231

    Article  Google Scholar 

  8. Caesar H, Uijlings J, Ferrari V (2016) Facial emotion recognition based on biorthogonal wavelet entropy, fuzzy support vector machine, and stratified cross validation. IEEE Acess 4(2016):8375–8385

    Google Scholar 

  9. Changxing D, Dacheng T (2015) Robust face recognition via multimodal deep face representation. IEEE Trans Multimed 17(11):2049–2058

    Article  Google Scholar 

  10. Chen Y, Hao C, Wu W, Wu E (2016) Robust dense reconstruction by range merging based on confidence estimation. SCIENCE CHINA Inf Sci 59(9):1–11

    Google Scholar 

  11. Felzenszwalb PF, Huttenlocher DP (2006) Efficient belief propagation for early vision. Int J Comput Vis 70(1):41–54

    Article  Google Scholar 

  12. Gu B, Sheng VS (2016) A robust regularization path algorithm for -support vector classification. IEEE Trans Neural Netw Learn Syst. doi:10.1109/TNNLS.2016.2527796

  13. Gu B, Sheng VS, Tay KY, Romano W, Li S (2015) Incremental support vector learning for ordinal regression. IEEE Trans Neural Netw Learn Syst 26(7):1403–1416

    Article  MathSciNet  Google Scholar 

  14. Gu B, Sheng VS, Wang Z, Ho D, Osman S, Li S (2015) Incremental learning for -Support Vector Regression. Neural Netw 67:140–150

    Article  Google Scholar 

  15. Gu B, Sun X, Sheng VS (2016) Structural minimax probability machine. IEEE Trans Neural Netw Learn Syst. doi:10.1109/TNNLS.2016.2527796

  16. Huang GB, Ramesh M, Berg T, Learned-Miller E (2007) Labeled faces in the wild: a database for studying face recognition in unconstrained environments. University of Massachusetts, Amherst, Tech. Rep. 7-49

  17. Jiang X, Lai J (2015) Sparse and dense hybrid representation via dictionary decomposition for face recognition. IEEE Trans Pattern Anal Mach Intell 37(5):1067–1079

    Article  Google Scholar 

  18. Jiang X, Mandal B, Kot A (2008) Eigenfeature regularization and extraction in face recognition. IEEE Trans Pattern Anal Mach Intell 30(3):383–394

    Article  Google Scholar 

  19. Kazuhiro F, Osamu Y (2005) Face recognition using multi-viewpoint patterns for robot vision. In: Proceedings of the eleventh international symposium on robotics research, pp 192–201

  20. Li SZ, Jain AK (2011) Handbook of face recognition. Springer, Berlin

    Book  MATH  Google Scholar 

  21. Li SZ, Lu J (1999) Face recognition using the nearest feature line method. IEEE Trans Neural Netw 10(2):439–443

    Article  Google Scholar 

  22. Li Y, Lu H, Li J, Li X, Li Y, Serikawa S (2016) Underwater image de-scattering and classification by deep neural network. Comput Electr Eng 2016(54):68–77

    Article  Google Scholar 

  23. Liu C, Yuen J, Torralba A (2011) Sift flow: dense correspondence across scenes and its applications. IEEE Trans Pattern Anal Mach Intell 33(5):978–994

    Article  Google Scholar 

  24. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  25. Lu HM, Li B, Zhu YJ, Li Y, Xu X, He L, Li X, Li JR, Serikawa S (2016) Wound intensity correction and segmentation with convolutional neural networks. Concurrency and computation: practice and experience

  26. Martinez AM (1998) The AR face database. CVC Techique Report

  27. Naseem I, Togneri R, Bennamoun M (2010) Linear regression for face recognition. IEEE Trans Pattern Anal Mach Intell 32(11):2106–2112

    Article  Google Scholar 

  28. Peng Y, Ganesh A, Wright J, Xu W, Ma Y (2012) Rasl: robust alignment by sparse and low-rank decomposition for linearly correlated images. IEEE Trans Pattern Anal Mach Intell 34(11):2233–2246

    Article  Google Scholar 

  29. Phillips PJ, Moon H, Rizvi S, Rauss PJ (2000) The FERET evaluation methodology for face-recognition algorithms. IEEE Trans Pattern Anal Mach Intell 22(10):1090–1104

    Article  Google Scholar 

  30. Samaria F, Harter A (1994) Parameterization of a stochastic model for human face identification. In: Proceedings of IEEE workshop on applications of computer vision

  31. Shao CB, Song XN, Shu X, Wu XJ (2016) Converted-face identification: using synthesized images to replace original images for recognition. Multimed Tools Appl 75:1–21

    Article  Google Scholar 

  32. Shen F, Yang WK, Li H, Zhang H, Shen HT (2016) Robust regression based face recognition with fast outlier removal. Multimed Tools Appl 75:12535–12546

    Article  Google Scholar 

  33. Turk M, Pentland A (2010) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86

    Article  Google Scholar 

  34. Viola P, Jones MJ (2004) Robust real-time face detection. Int J Comput Vis 57(3):137–154

    Article  Google Scholar 

  35. Wagner A, Wright J, Ganesh A, Zhou Z, Mobahi H, Ma Y (2012) Toward a practical face recognition system: Robust alignment and illumination by sparse representation. IEEE Trans Pattern Anal Mach Intell 34(2):372–386

    Article  Google Scholar 

  36. Wang J, Yang J, Yu K, Lv F, Huang T, Gong Y (2010) Locality constrained linear coding for image classification. In: Proceedings of IEEE international conference on computer vision and pattern recognition, pp 3360–3367

  37. Wang W, Yang J, Xiao J, Li S, Zhou D (2015) Face recognition based on deep learning. Human Centered Computing 812–820

  38. Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227

    Article  Google Scholar 

  39. Yang M, Zhang L (2010) Gabor feature based sparse representation for face recognition with Gabor occlusion dictionary. In: Proceedings of Europe conference on computer vision, pp 448–461

  40. Yang M, Zhang L, Feng X, Zhang D (2014) Sparse representation based Fisher discrimination dictionary learning for image classification. In: Proceedings of IEEE international conference on computer vision, pp 209–232

  41. Zhang DY, Wang S, Phillips P, Yang J, Yuan TF (2016) Three-dimensional eigenbrain for the detection of subjects and brain regions related with alzheimer’s disease. J Alzheimers Dis 50(4):1163–1179

    Article  Google Scholar 

  42. Zhang L, Zhou WD, Li FZ (2015) Kernel sparse representation-based classifier ensemble for face recognition. Multimed Tools Appl 74:123–137

    Article  Google Scholar 

  43. Zhao W, Chellappa R, Phillips PJ, Rosenfeld A (2003) Face recognition: a literature survey. ACM Comput Surv 35(4):399–458

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the associated editor and all the anonymous reviewers for their valuable comments and suggestions. This work was partly supported by the National Science Foundation (Grant No. IIS-1302164), and the National Natural Science Foundation of China (Grant No. 61401228, 61402122, 61571240, 61501247, 61501259, 61671253), and China Postdoctoral Science Foundation (Grant No. 2015M581841), and Natural Science Foundation of Jiangsu Province (Grant No. BK20160908), and Postdoctoral Science Foundation of Jiangsu Province (Grant No. 1501019A), and the Priority Academic Program Development of Jiangsu Higer Education Institutions(PAPD), and Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology(CICAEET), and Nanjing University of Information Science and Technology Research Foundation for Talented Scholars (Grant No. 2015r014).

Author information

Authors and Affiliations

  1. Key Lab of Ministry of Education for Broad Band Communication and Sensor Network Technology, Nanjing University of Posts and Telecommunications, Nanjing, China

    Quan Zhou, Yawen Fan, Hu Zhu, Xiaofu Wu & Weiping Zhu

  2. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China

    Quan Zhou & Wenbin Yu

  3. The State Grid HuBei Information and Telecommunication Company, Wuhan, China

    Cheng Zhang

  4. Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, China

    Wenbin Yu

  5. School of Big Data and Computer Science, Guizhou Normal University, Guiyang, China

    Weihua Ou

  6. Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada

    Weiping Zhu

  7. Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA

    Longin Jan Latecki

Authors
  1. Quan Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cheng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenbin Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yawen Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hu Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiaofu Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Weihua Ou
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Weiping Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Longin Jan Latecki
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Quan Zhou.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, Q., Zhang, C., Yu, W. et al. Face recognition via fast dense correspondence. Multimed Tools Appl 77, 10501–10519 (2018). https://doi.org/10.1007/s11042-017-4569-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-017-4569-1

Keywords

Search

Navigation