Skip to main content
SpringerLink
Log in

Performance Evaluation of Facenet on Low Resolution Face Images

  • Conference paper
  • First Online:
Communication, Networks and Computing (CNC 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 839))

Included in the following conference series:

Abstract

In recent years, deep learning has become a very prevalent technology in face recognition. Google came up with a deep convolution neural network called Facenet which performs face recognition using only 128 bytes per face. As claimed by Google, Facenet attained nearly 100-percent accuracy on the widely used Labeled Faces in the Wild (LFW) dataset. But in the case of low resolution face images it’s the other way round. This low resolution challenge occurs in many existing face recognition algorithms, due to which satisfactory performance has become hard to be achieved. The goal of this paper is to present the obtained results after evaluating the performance of Facenet on low resolution face images compared to high resolution face images.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Zhao, W., Chellappa, R.: Face recognition, a literature survey. ACM Comput. Surv. (CSUR) 35(4), 399–458 (2003). National Institute of Standards and Technology

    Article  Google Scholar 

  2. Ding, C., Choi, J., Tao, D., Davis, L.S.: Multi-directional multi-level dual-cross patterns for robust face recognition. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 3, pp. 518–531 (2016)

    Article  Google Scholar 

  3. Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, pp. 815–823 (2015)

    Google Scholar 

  4. Learned-Miller, E., Huang, G.B., RoyChowdhury, A., Li, H., Hua, G.: Labeled faces in the wild: a survey. In: Kawulok, M., Celebi, M., Smolka, B. (eds.) Advances in Face Detection and Facial Image Analysis, pp. 189–248. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-25958-1_8

    Chapter  Google Scholar 

  5. Lu, Z., Jiang, X., Kot, A.: Enhance deep learning performance in face recognition. In: 2017 2nd International Conference on Image, Vision and Computing (ICIVC), Chengdu, pp. 244–248 (2017)

    Google Scholar 

  6. 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 (2005)

    Google Scholar 

  7. Raghavendra, R., Raja, K.B., Yang, B., Busch, C.: Comparative evaluation of super- resolution techniques for multi-face recognition using light-field camera. In: 2013 18th International Conference on Digital Signal Processing (DSP), Fira, pp. 1–6 (2013)

    Google Scholar 

  8. Jia, K., Gong, S.G.: Multi-modal tensor face for simultaneous super-resolution and recognition. In: Proceedings of IEEE 10th International Conference on Computer Vision (ICCV), October 2005, Beijing, China, vol. 2, pp. 1683–1690 (2005)

    Google Scholar 

  9. Choi, J.Y., Ro, Y.M., Plataniotis, K.N.: Color face recognition for degraded face images. IEEE Trans. Syst. Man Cybern. Part B Cybern. 39(5), 1217–1230 (2009)

    Article  Google Scholar 

  10. Biswas, S., Bowyer, K.W., Flynn, P.J.: Multidimensional scaling for matching low-resolution face images. IEEE Trans. Pattern Anal. Mach. Intell. 34(10), 2019–2030 (2012)

    Article  Google Scholar 

  11. Ren, C.X., Dai, D.Q., Yan, H.: Coupled kernel embedding for low-resolution face recognition. IEEE Trans. Image Process. 21(8), 3770–3783 (2012)

    Article  MathSciNet  Google Scholar 

  12. Ghazi, M.M., Ekenel, H.K.: A comprehensive analysis of deep learning based representation for face recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Las Vegas, NV, pp. 102–109 (2016)

    Google Scholar 

  13. Krizhevsky, A., Sutskeyer, I.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, NIPS 2012, vol. 1, pp. 1097–1105 (2012)

    Google Scholar 

  14. Szegedy, C., et al.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, pp. 1–9 (2015). https://doi.org/10.1109/CVPR.7298594

  15. Ouyang, W., et al.: DeepID-Net: deformable deep convolutional neural networks for object detection. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, pp. 2403–2412 (2015)

    Google Scholar 

  16. Wang, X.G., Tang, X.O.: Hallucinating face by eigen transformation. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 35(3), 425–434 (2005)

    Article  Google Scholar 

  17. Laaksonen, J., Oja, E.: Classification with learning k-nearest neighbors. In: 1996 IEEE International Conference on Neural Networks, Washington, DC, vol. 3, pp. 1480–1483 (2002)

    Google Scholar 

  18. Kurmi, U.S., Agrawal, D., Baghel, R.K.: Study of different face recognition algorithms and challenges. Int. J. Eng. Res. 3, 112–115 (2014). https://doi.org/10.17950/ijer/v3s2/216

    Article  Google Scholar 

  19. Li, Z., Hou, Y., Liu, H., Li, X.: Very low resolution face reconstruction based on multi-output regression. In: 2014 IEEE Workshop on Electronics, Computer and Applications, Ottawa, ON, pp. 74–77 (2014)

    Google Scholar 

  20. Shi, J., Qi, C.: From local geometry to global structure: learning latent subspace for low-resolution face image recognition. IEEE Signal Process. Lett. 22(5), 554–558 (2015)

    Article  Google Scholar 

  21. Xu, Y., Jin, Z.: Down-sampling face images and low-resolution face recognition. In: 3rd International Conference on Innovative Computing Information and Control, Dalian, Liaoning, p. 392 (2008)

    Google Scholar 

  22. Lee, S.-W., Park, J., Lee, S.-W.: Low resolution face recognition based on support vector data description. Pattern Recognit. 39(9), 1809–1812 (2006). ISSN 0031-3203

    Article  Google Scholar 

  23. Dai, Y., Nakanom Y.: Extraction for facial images from complex background using color information and SGLD matrices. In: 1st International Workshop on Automatic Face and Gesture Recognition, pp. 238–242 (1995)

    Google Scholar 

  24. Yang, G., Huang, T.S.: Human face detection in a complex background. Pattern Recognit. 27, 53–63 (1994)

    Article  Google Scholar 

  25. Garcia, C., Tziritas, G.: Face detection using quantized skin color regions merging and wavelet packet analysis. IEEE Trans. Multimed. 1, 264–277 (1999)

    Article  Google Scholar 

  26. Wu, H., Yokoyama, T., Pramadihanto, D., Yachida, M.: Face and facial feature extraction from color image. In: 2nd International Conference on Automatic Face and Gesture Recognition, pp. 345–350 (1996)

    Google Scholar 

  27. Sarkar, R., Bakshi, S., Sa, P.K.: A real-time model for multiple human face tracking from low-resolution surveillance videos. Procedia Technol. 6, 1004–1010 (2012). ISSN 2212-0173

    Article  Google Scholar 

  28. Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23(10), 1499–1503 (2016)

    Article  Google Scholar 

  29. Kaur, R., Himanshi, E.: Face recognition using principal component analysis. In: IEEE International Advance Computing Conference (IACC), Banglore, pp. 585–589 (2015)

    Google Scholar 

  30. Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53

    Chapter  Google Scholar 

  31. Pnevmatikakis, A., Polymenakos, L.: Far-field, multi-camera, video-to-video face recognition. In: Delac, K., Grgic, M. (eds.) Face Recognition, pp. 467–486 (2007)

    Google Scholar 

  32. Nazari, S., Moin, M.S.: Face recognition using global and local Gabor features. In: 2013 21st Iranian Conference on Electrical Engineering (ICEE), Mashhad, pp. 1–4 (2013)

    Google Scholar 

  33. Kurita, T., Otsu, N., Sato, T.: A face recognition method using higher order local autocorrelation and multivariate analysis. In: Proceedings of IEEE 11th International Conference on Pattern Recognition (ICPR), The Hague, The Netherlands, vol. B, pp. 213–216 (1992)

    Google Scholar 

  34. Xingjing, D., Dongmei, Z., Hongyun, Z.: Study of fast Adaboost face detection algorithm. In: International Conference on Computer Application and System Modeling (ICCASM 2010), Taiyuan, pp. V6-136–V6-139 (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, India

    Monika Rani Golla & Poonam Sharma

Authors
  1. Monika Rani Golla
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Poonam Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Monika Rani Golla .

Editor information

Editors and Affiliations

  1. Indian Institute of Information Technology, Allahabad, India

    Shekhar Verma

  2. ITM University, Gwalior, India

    Ranjeet Singh Tomar

  3. ITM University, Gwalior, India

    Brijesh Kumar Chaurasia

  4. Indian Institute of Information Technology, Allahabad, India

    Vrijendra Singh

  5. School of Information Technology, Deakin University, Burwood, VIC, Australia

    Jemal Abawajy

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Golla, M.R., Sharma, P. (2019). Performance Evaluation of Facenet on Low Resolution Face Images. In: Verma, S., Tomar, R., Chaurasia, B., Singh, V., Abawajy, J. (eds) Communication, Networks and Computing. CNC 2018. Communications in Computer and Information Science, vol 839. Springer, Singapore. https://doi.org/10.1007/978-981-13-2372-0_28

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-2372-0_28

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2371-3

  • Online ISBN: 978-981-13-2372-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation