Skip to main content
SpringerLink
Log in

Unsupervised face recognition in the wild using high-dimensional features under super-resolution and 3D alignment effect

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Face recognition algorithms customarily utilize query faces captured from uncontrolled, in the wild, environments. The quality of these facial images is affected by various internal factors, including the quality of sensors used in outdoor cameras as well as external ones, such as the quality and direction of light. These factors adversely affect the overall quality of the captured images often causing blurring and/or low resolution, a phenomena commonly referred to as image degradation. Super-resolution algorithms are highly effective in improving the resolution of degraded images, more so if the captured face is small requiring scaling up. With this motivation, this research aims at demonstrating the effect of one of the state-of-the-art image super-resolution algorithms on the labeled faces in the wild (lfw) dataset. In this regard, several cases are analyzed to demonstrate the effectiveness of the super-resolution algorithm. Each case is then investigated independently comparing the order of execution before or after the 3D face alignment step. Following this, resulting images are tested on a closed set face recognition protocol using unsupervised algorithms with high-dimensional extracted features. The inclusion of super-resolution resulted in improvement in the recognition rate compared to unsupervised algorithm results reported in the literature.

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
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. Minimum detection size of Haar Cascade classifier for face detection is 24 \(\times \) 24

  2. Python wrapper for dlib and OpenCV libraries.

  3. Adaboost Haar Cascade classifier is known to have higher face detection rate but with large number of false positives [1, 25].

References

  1. Abualkibash, M., ElSayed, A., Mahmood, A.: Highly scalable, parallel and distributed adaboost algorithm using light weight threads and web services on a network of multi-core machines. CoRR arXiv:1306.1467 (2013)

  2. Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006). https://doi.org/10.1109/TPAMI.2006.244

    Article  MATH  Google Scholar 

  3. Banerjee, P.K., Datta, A.K.: Band-pass correlation filter for illumination- and noise-tolerant face recognition. Signal Image Video Processing. 11(1), 9–16 (2017). https://doi.org/10.1007/s11760-016-0882-9

    Article  Google Scholar 

  4. Beham, M.P., Roomi, S.M.M.: Anti-spoofing enabled face recognition based on aggregated local weighted gradient orientation. Signal Image Video Processing. (2017). https://doi.org/10.1007/s11760-017-1189-1

  5. Chen, D., Cao, X., Wen, F., Sun, J.: Blessing of dimensionality: High-dimensional feature and its efficient compression for face verification. In: Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, CVPR ’13, pp. 3025–3032. IEEE Computer Society, Washington, DC, USA (2013). https://doi.org/10.1109/CVPR.2013.389

  6. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). https://doi.org/10.1109/CVPR.2005.177

  7. Ding, C., Bao, T., Karmoshi, S., Zhu, M.: Single sample per person face recognition with kpcanet and a weighted voting scheme. Signal Image Video Processing. 11(7), 1213–1220 (2017). https://doi.org/10.1007/s11760-017-1077-8

    Article  Google Scholar 

  8. Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016). https://doi.org/10.1109/TPAMI.2015.2439281

    Article  Google Scholar 

  9. Dreuw, P., Steingrube, P., Hanselmann, H., Ney, H.: Surf-face: face recognition under viewpoint consistency constraints. In: Proceedings of BMVC, pp. 7.1–7.11 (2009). https://doi.org/10.5244/C.23.7

  10. ElSayed, A., Mahmood, A., Sobh, T.: Unsupervised Sub-graph Selection and Its Application in Face Recognition Techniques, pp. 247–256. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20801-5_27

    Google Scholar 

  11. ElSayed, A., Mahmood, A., Sobh, T.M.: Effect of super resolution on high dimensional features for unsupervised face recognition in the wild. CoRR arXiv:1704.01464 (2017)

  12. Fookes, C., Lin, F., Chandran, V., Sridharan, S.: Evaluation of image resolution and super-resolution on face recognition performance. J. Vis. Commun. Image Represent. 23(1), 75–93 (2012). https://doi.org/10.1016/j.jvcir.2011.06.004

    Article  Google Scholar 

  13. Geng, C., Jiang, X.: Face recognition using sift features. In: Proceedings of the 16th IEEE International Conference on Image Processing, ICIP’09, pp. 3277–3280. IEEE Press, Piscataway, NJ, USA (2009)

  14. Hassner, T., Harel, S., Paz, E., Enbar, R.: Effective face frontalization in unconstrained images. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)

  15. Hu, S., Maschal, R., Young, S.S., Hong, T.H., Phillips, P.J.: Face recognition performance with superresolution. Appl. Opt. 51(18), 4250–4259 (2012). https://doi.org/10.1364/AO.51.004250

    Article  Google Scholar 

  16. Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical Report 07-49, University of Massachusetts, Amherst (2007)

  17. Jin, W., Gong, F., Zeng, X., Fu, R.: Illumination robust face recognition using random projection and sparse representation. Signal Image Video Processing. (2017). https://doi.org/10.1007/s11760-017-1213-5

  18. Kazemi, V., Sullivan, J.: One millisecond face alignment with an ensemble of regression trees. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR ’14, pp. 1867–1874. IEEE Computer Society, Washington, DC, USA (2014). https://doi.org/10.1109/CVPR.2014.241

  19. Kong, Y., Zhang, S., Cheng, P.: Super-resolution reconstruction face recognition based on multi-level FFD registration. Optik Int. J. Light Electron Opt. 124(24), 6926–6931 (2013). https://doi.org/10.1016/j.ijleo.2013.05.175

    Article  Google Scholar 

  20. Learned-Miller, G.B.H.E.: Labeled faces in the wild: Updates and new reporting procedures. Technical Report of UM-CS-2014-003, University of Massachusetts, Amherst (2014)

  21. Liao, S., Lei, Z., Yi, D., Li, S.Z.: A benchmark study of large-scale unconstrained face recognition. In: IEEE International Joint Conference on Biometrics, pp. 1–8 (2014). https://doi.org/10.1109/BTAS.2014.6996301

  22. Lin, F., Fookes, C., Chandran, V., Sridharan, S.: Super-Resolved Faces for Improved Face Recognition from Surveillance Video, pp. 1–10. Springer, Berlin (2007). https://doi.org/10.1007/978-3-540-74549-5_1

  23. Rasti, P., Uiboupin, T., Escalera, S., Anbarjafari, G.: Convolutional Neural Network Super Resolution for Face Recognition in Surveillance Monitoring, pp. 175–184. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41778-3_18

    Google Scholar 

  24. Tang, Z., Wu, X., Leng, X., Chen, W.: A fast face recognition method based on fractal coding. Signal Image Video Processing. 11(7), 1221–1228 (2017). https://doi.org/10.1007/s11760-017-1078-7

    Article  Google Scholar 

  25. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, vol. 1, pp. I-511–I-518 (2001). https://doi.org/10.1109/CVPR.2001.990517

  26. Wheeler, F.W., Liu, X., Tu, P.H.: Multi-frame super-resolution for face recognition. In: 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–6 (2007). https://doi.org/10.1109/BTAS.2007.4401949

  27. Zhang, L., Chen, J., Lu, Y., Wang, P.: Face recognition using scale invariant feature transform and support vector machine. In: The 9th International Conference for Young Computer Scientists, 2008. ICYCS 2008, pp. 1766–1770 (2008)

Download references

Author information

Authors and Affiliations

  1. 221 University Ave, Bridgeport, CT, 06604, USA

    Ahmed ElSayed, Elif Kongar, Ausif Mahmood & Tarek Sobh

Authors
  1. Ahmed ElSayed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elif Kongar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ausif Mahmood
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tarek Sobh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ahmed ElSayed.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

ElSayed, A., Kongar, E., Mahmood, A. et al. Unsupervised face recognition in the wild using high-dimensional features under super-resolution and 3D alignment effect. SIViP 12, 1353–1360 (2018). https://doi.org/10.1007/s11760-018-1289-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-018-1289-6

Keywords

Search

Navigation