Skip to main content
SpringerLink
Log in

Multiple histogram-based face recognition with high speed FPGA implementation

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

Abstract

Face recognition is an algorithm that is capable of identifying or verifying a query face from multiple faces in the enrollment database. It poses a challenging problem in the field of image analysis and computer vision, especially for applications that deal with video sequences, face re-identification, or operate on intensity images and require fast processing. In this work, we introduce a high speed face recognition technique along with a high speed FPGA implementation. It uses a new similarity measure to estimate the distance between the query face and each of the database face images. The distance metric is the sum of the standard deviations between multiple histograms, which are calculated from each row of the query and database images. The lowest distance score refers to the database face that matches the query. The proposed technique is independent from the ambient illumination and outperforms the well-known face recognition algorithm “Eigenfaces” (it performs the face recognition 16× faster when both algorithms run on the same platform). Furthermore, we exploit data parallelism in our proposed algorithm to design a hardware accelerator and to implement it on an FPGA prototyping board. The results show 10x execution time improvement in comparison to the software version.

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
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Notes

  1. www.digilentinc.com/

References

  1. Bartlett MS, Movellan JR, Sejnowski TJ (2002) Face recognition by independent component analysis. IEEE Trans Neural Netw 1:1450–1464

    Article  Google Scholar 

  2. Bateux Q, Marchand E (2017) Histograms-based visual servoing. IEEE Robot Autom Lett 2(1):80–87

    Article  Google Scholar 

  3. Bedagkar-Gala A, Shah SK (2014) A survey of approaches and trends in person re-identification. Image Vision Comput 32(4):270–286. https://doi.org/10.1016/j.imavis.2014.02.001

    Article  Google Scholar 

  4. Beheshti I, Maikusa N, Matsuda H, Demirel H, Anbarjafari G (2017) Histogram-based feature extraction from individual gray matter similarity-matrix for alzheimers disease classification. J Alzheimers Dis 55(4):1571–1582

    Article  Google Scholar 

  5. Cha SH, Srihari SN (2002) On measuring the distance between histograms. Pattern Recogn 35(6):1355– 1370

    Article  MATH  Google Scholar 

  6. Demirel H, Anbarjafari G (2008) Pose invariant face recognition using probability distribution functions in different color channels. IEEE Signal Process Lett 15:537–540

    Article  Google Scholar 

  7. Déniz O, Bueno G, Salido J, De la Torre F (2011) Face recognition using histograms of oriented gradients. Pattern Recogn Lett 32(12):1598–1603

    Article  Google Scholar 

  8. Georghiades A, Belhumeur P, Kriegman D (1997) Yale face database. In: Center for computational vision and control at Yale University, vol 2. http://cvc.yale.edu/projects/yalefaces

  9. Hietmeyer R (2000) Biometric identification promises fast and secure processing of airline passengers. Int Civil Aviat Org J 55(9):10–11

    Google Scholar 

  10. Huang SC, Cheng FC, Chiu YS (2013) Efficient contrast enhancement using adaptive gamma correction with weighting distribution. Trans Image Process 22 (3):1032–1041. https://doi.org/10.1109/TIP.2012.2226047

    Article  MathSciNet  MATH  Google Scholar 

  11. Inc X. (2014) 7 Series FPGAs Overview, vol 1. Xilinx

  12. Kar A, Sarkar S, Bhattacharjee D (2017) Local centre of mass face for face recognition under varying illumination. Multimed Tools Appl 76(18):19211–19240

    Article  Google Scholar 

  13. Leung HY, Cheng LM, Li XY (2015) A fpga implementation of facial feature extraction. J Real-Time Image Process 10:135–149. https://doi.org/10.1007/s11554-012-0263-8

    Article  Google Scholar 

  14. Li C, Yee LY, Maruyama H, Yamaguchi Y (2017) Fpga-based volleyball player tracker. SIGARCH Comput Archit News 44:80–86. https://doi.org/10.1145/3039902.3039917

    Article  Google Scholar 

  15. Liu C, Wechsler H (2002) Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. Trans Image Process 11(4):467–476. https://doi.org/10.1109/TIP.2002.999679

    Article  Google Scholar 

  16. Malik A, Salcic Z, Chong C, Javed S (2013) System-level approach to the design of a smart distributed surveillance system using systemj. ACM Trans Embedded Comput Syst (TECS) 11(4):77:1–77:24. https://doi.org/10.1145/2362336.2362344

    Google Scholar 

  17. Phama TTT, Lea T, Vua H, Daoa TK, Nguyen VT (2017) Fully-automated person re-identification in multi-camera surveillance system with a robust kernel descriptor and effective shadow removal method. Elsevier Image Vis Comput 59:44–62

    Article  Google Scholar 

  18. Phillips PJ, Moon H, Rizvi SA, Rauss PJ (2000) The feret evaluation methodology for face-recognition algorithms. IEEE Trans Pattern Anal Mach Intell 22 (10):1090–1104. https://doi.org/10.1109/34.879790

    Article  Google Scholar 

  19. Phillips JP et al (2003) Face recognition vendor test 2002. In: IEEE international workshop on analysis and modeling of faces and gestures. IEEE Computer Society

  20. Phillips PJ, Flynn PJ, Scruggs T, Bowyer KW, Chang J, Hoffman K, Marques J, Min J, Worek W (2005) Overview of the face recognition grand challenge. In: Proceedings of the 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05) - Volume 1, CVPR ’05. IEEE Computer Society, Washington, pp 947–954. https://doi.org/10.1109/CVPR.2005.268

  21. Savvides M, Kumar BVKV, Khosla PK (2004) “igenphases vs. eigenfaces”. In: ICPR (3). IEEE Computer Society, pp 810–813

  22. Senouci B, Charfi I, Heyrman B, Dubois J, Miteran J (2016) Fast prototyping of a soc-based smart-camera: a real-time fall detection case study. J Real-Time Image Process 12(4):649–662. https://doi.org/10.1007/s11554-014-0456-4

    Article  Google Scholar 

  23. Shan Y, Hao Y, Wang W, Wang Y, Chen X, Yang H, Luk W (2014) Hardware acceleration for an accurate stereo vision system using mini-census adaptive support region. ACM Trans Embed Comput Syst (TECS) 13(4s):132:1–132:24. https://doi.org/10.1145/2584659

    Google Scholar 

  24. Smitha KG, Vinod AP (2015) Facial emotion recognition system for autistic children: a feasible study based on FPGA implementation. Med Biol Eng Comput 53(11):1221–1229. https://doi.org/10.1007/s11517-015-1346-z

    Article  Google Scholar 

  25. Sun Y, Wang X, Tang X (2014) Deep learning face representation from predicting 10,000 classes. In: Proceedings of the 2014 IEEE conference on computer vision and pattern recognition, CVPR ’14, pp. 1891–1898. IEEE Computer Society, Washington. https://doi.org/10.1109/CVPR.2014.244

  26. Taigman Y, Yang M, Ranzato M, Wolf L (2014) Deepface: closing the gap to human-level performance in face verification. In: Proceedings of the 2014 IEEE conference on computer vision and pattern recognition, CVPR ’14, pp 1701–1708. IEEE Computer Society, Washington. https://doi.org/10.1109/CVPR.2014.220

  27. Tian L, Fan C, Ming Y (2016) Learning spherical hashing based binary codes for face recognition. Multimed Tools Appl 76(11):13271–13299

    Article  Google Scholar 

  28. Turk M (2013) Over twenty years of eigenfaces. TOMCCAP 9(1s):45:1–45:5

    Article  Google Scholar 

  29. Turk M, Pentland A (1991) Eigenfaces for recognition. J. Cogn Neurosci 3(1):71–86. https://doi.org/10.1162/jocn.1991.3.1.71

    Article  Google Scholar 

  30. what-when how.com: Introduction to face recognition (2014). http://what-when-how.com/face-recognition

  31. Xilinx I (2012) AXI Reference Guide, vol 14. Xilinx

  32. Xilinx: Vivado design suite - hlx editions (2016). www.xilinx.com/products/design-tools/vivado.html

  33. Yin DBM, Omar S, Talip BA, Muklas A, Norain NAM, Othman AT (2017) Fusion of face recognition and facial expression detection for authentication: a proposed model. In: Proceedings of the 11th international conference on ubiquitous information management and communication, IMCOM ’17. ACM, New York, pp 21:1–21:8. https://doi.org/10.1145/3022227.3022247

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Sharjah, Sharjah, United Arab Emirates

    Talal Bonny & Tamer Rabie

  2. Hasan Kalyoncu University, Gaziantep, Turkey

    A. H. Abdul Hafez

Authors
  1. Talal Bonny
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tamer Rabie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. H. Abdul Hafez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Talal Bonny.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bonny, T., Rabie, T. & Hafez, A.H.A. Multiple histogram-based face recognition with high speed FPGA implementation. Multimed Tools Appl 77, 24269–24288 (2018). https://doi.org/10.1007/s11042-018-5647-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-5647-8

Keywords

Search

Navigation