[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

CFSM: a novel frame analyzing mechanism for real-time face recognition system on the embedded system

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

Abstract

The development of web cameras and smart phones is mature, and more and more facial recognition-related applications are implemented on embedded systems. The demand for real-time face recognition on embedded systems is also increasing. In order to improve the accuracy of face recognition, most of the modern face recognition systems consist of multiple deep neural network models for recognition. However, in an embedded system, integrating these complex neural network models and execute simultaneously is not easy to achieve the goal of real-time recognition of human faces and their identities. In view of this, this study proposes a new frame analysis mechanism, continuous frames skipping mechanism (CFSM), which can analyze the frame in real time to determine whether it is necessary to perform face recognition on the current frame. Through the analysis of CFSM, the frames that do not need to be re-recognized for face are omitted. In this way, the workload of the face recognition system will be greatly reduced to achieve the goal of real-time face recognition in the embedded system. The experimental results show that the proposed CFSM mechanism can greatly increase the speed of face recognition in the video on the embedded system, achieving the goal of real-time face recognition.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

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
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Abu-El-Haija S, et al (2016) Youtube-8m: a large-scale video classification benchmark. arXiv, abs:1609.08675

  2. Chen S et al (2018) MobileFaceNets: efficient CNNs for accurate real-time face verification on mobile devices. Chinese conference on biometric recognition 2018. Lecture notes in computer science, 10996, pp 428–438

  3. Dabhade SB, et al (2017) Double layer PCA based hyper spectral face recognition using KNN classifier. International conference on current trends in computer, electrical, electronics and communication (CTCEEC), pp 289–293

  4. Dadi HS, Pillutla GM (2016) Improved face recognition rate using HOG features and SVM classifier. IOSR J Electron Commun Eng IOSR-JECE 11:34–44

    Article  Google Scholar 

  5. Danelljan M et al (2014) Accurate scale estimation for robust visual tracking. Proceedings of the British machine vision conference, pp 1–5

  6. Deng J et al (2019) ArcFace: additive angular margin loss for deep face recognition. IEEE conference on computer vision and pattern recognition (CVPR), pp 4685–4694

  7. Dhamecha TI (2016) On frame selection for video face recognition. In: Kawulok M (ed) Proceedings of advances in face detection and facial image analysis. Springer, Cham, pp 279–297

  8. Gygli M (2018) Ridiculously fast shot boundary detection with fully convolutional neural networks. International conference on content-based multimedia indexing (CBMI), pp 1–4

  9. Hassanien A et al (2017) Large-scale, fast and accurate shot boundary detection through spatio-temporal convolutional neural networks. arXiv, abs:1705.03281

  10. He K et al (2016) Deep residual learning for image recognition. IEEE conference on computer vision and pattern recognition (CVPR), pp 770–778

  11. He Q, He B, Zhang Y et al (2019) Multimedia based fast face recognition algorithm of speed up robust features. Multimed Tools Appl 78:24035–24045

    Article  Google Scholar 

  12. Holmes SA, Klein G, Murray DW (2008) An O(N2) square root unscented Kalman filter for visual simultaneous localization and mapping. IEEE Trans Pattern Anal Mach Intell 31(7):1251–1263

    Article  Google Scholar 

  13. Huang GB et al (2007) Labeled Faces in the wild: a database for studying face recognition in unconstrained environments. University of Massachusetts, pp 7–49

  14. Jain AK, Ross AA, Nandakumar K (2011) Introduction to biometrics. Springer Science & Business Media, Berlin, pp 111–117

    Book  Google Scholar 

  15. Jin X et al (2020) Efficient blind face recognition in the cloud. Multimed Tools Appl 79:12533–12550

    Article  Google Scholar 

  16. Jose E et al (2019) Face recognition based surveillance system using FaceNet and MTCNN on Jetson TX2. 5th International conference on advanced computing & communication systems (ICACCS), pp 608–613

  17. Kuhn HW (1955) The Hungarian method for the assignment problem. Naval Res Logist Q 2(1–2):83–97

    Article  MathSciNet  Google Scholar 

  18. Liu W et al (2017. SphereFace: deep hypersphere embedding for face recognition. IEEE conference on computer vision and pattern recognition (CVPR), pp 6738–6746

  19. Murray S (2017) Real-time multiple object tracking-a study on the importance of speed. arXiv abs:1709.03572

  20. Parveen P, Thuraisingham B (2016) Face recognition using multiple classifiers. 18th IEEE international conference on tools with artificial intelligence, pp 179–186

  21. Qi X, Liu C, Schuckers S (2016) Key-frame analysis for face related video on GPU-Accelerated embedded platform. International conference on computational science and computational intelligence (CSCI), pp 682–687

  22. Saez-Trigueros D, Meng L, Hartnett M (2018) Face recognition: from traditional to deep learning methods. arXiv, abs/1811.00116

  23. Sajjad M et al (2020) Raspberry Pi assisted face recognition framework for enhanced law-enforcement services in smart cities. Futur Gener Comput Syst 108:995–1007

    Article  Google Scholar 

  24. Saypadith S, Aramvith S (2018) Real-time multiple face recognition using deep learning on embedded GPU System. Asia-Pacific signal and information processing association annual summit and conference (APSIPA ASC), pp 1318–1324

  25. Schroff F, Kalenichenko D, Philbin J (2015) FaceNet: a unified embedding for face recognition and clustering. IEEE conference on computer vision and pattern recognition (CVPR), pp 815–823

  26. Shi X et al (2018) Real-time rotation-invariant face detection with progressive calibration networks. IEEE conference on computer vision and pattern recognition (CVPR), pp 2295–2303

  27. Sinha D, El-Sharkawy M (2019) Thin MobileNet: an enhanced MobileNet architecture. IEEE 10th annual ubiquitous computing, electronics & mobile communication conference (UEMCON), pp 280–285

  28. Stekas N, van den Heuvel D (2016) Face recognition using local binary patterns histograms (LBPH) on an FPGA-Based system on chip (SoC). IEEE international parallel and distributed processing symposium workshops (IPDPSW), pp 300–304

  29. Sujay SN, Reddy HM, Ravi J (2017) Face recognition using extended LBP features and multilevel SVM classifier. International conference on electrical, electronics, communication, computer, and optimization techniques, pp 1–4

  30. Sun Y, Wang X, Tang X (2014) Deep learning face representation from predicting 10,000 classes. IEEE conference on computer vision and pattern recognition, pp 1891–1898

  31. Taigman Y et al (2014) DeepFace: closing the gap to human-level performance in face verification. IEEE conference on computer vision and pattern recognition, pp 1701–1708

  32. Tran D et al (2015) Learning spatiotemporal features with 3D convolutional networks. Proceedings of the IEEE international conference on computer vision

  33. Viola P, Jones MJ (2004) Robust real-time face detection. Int J Comput Vision 57(2):137–154

    Article  Google Scholar 

  34. Wang H et al (2018) CosFace: large margin cosine loss for deep face recognition. IEEE conference on computer vision and pattern recognition (CVPR), pp 5265–5274

  35. Yang F, Paindavoine M (2003) Implementation of an RBF neural network on embedded systems: real-time face tracking and identity verification. IEEE Trans Neural Netw 14(5):1162–1175

    Article  Google Scholar 

  36. Zhang K et al (2016) Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process Lett 23(10):1499–1503

    Article  Google Scholar 

  37. Zhang M et al (2019) Embedded face recognition system based on multi-task convolutional neural network and LBP features. IEEE international conference of intelligent applied systems on engineering (ICIASE), pp 132–135

  38. Zhang S et al (2019) Faceboxes: a CPU real-time and accurate unconstrained face detector. Neurocomputing 364:297–309

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported in part by the Ministry of Science and Technology of Republic of China, Taiwan under Grant MOST 105-2221-E-033-047.

Author information

Authors and Affiliations

  1. Department of Information and Computer Engineering, Chung Yuan Christian University, Chung Li District, Taoyuan, Taiwan

    Slo-Li Chu, Chien-Fang Chen & Yu-Chen Zheng

Authors
  1. Slo-Li Chu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chien-Fang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yu-Chen Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Slo-Li Chu.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chu, SL., Chen, CF. & Zheng, YC. CFSM: a novel frame analyzing mechanism for real-time face recognition system on the embedded system. Multimed Tools Appl 81, 1867–1891 (2022). https://doi.org/10.1007/s11042-021-11599-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-021-11599-0

Keywords

Search

Navigation