Skip to main content
SpringerLink
Log in

JULive3D: a live image acquisition protocol for real-time 3D face recognition

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

Abstract

The capturing and development of a real time 3D face recognition system is always a big challenge and attracts huge popularity nowadays. This is because, in most security systems employing face recognition, it is always crucial to capture live images and recognize them. This should save time and cost as well, and incredibly is acceptable across all areas of security concerns. This paper deals with the development of an entirely new 3D face recognition system which was developed in Jadavpur University with six subjects captured fully in real time. The main problem which we have tried to address in the present work is that, how various issues like camera calibration, alignment of the camera, distance of the camera from the subject affects the facial recognition rate. We will also analyze how facial registration helps to increase the recognition rates which have been imposed by the above factors. The problem is relevant, because, it’s always a challenge to capture and correctly predict a real time system. The necessary problems needed for setting up a real time system is always a matter to be investigated by the researchers in face recognition domain. The main overview of our present proposed method is to capture the subjects in real-time taking into consideration all the calibration issues like camera alignment, distance of camera from the subject, and several other factors. We have also discussed how these factors affect the recognition rate. Once captured, we have tried to justify the varying recognition rate of the subjects due to the changes in calibration, alignment and other issues. Now, in order to improve the performances of the subjects, we have proposed a new 3D face registration algorithm termed as FaRegAvFM8, which was tested on the subjects from our database acquired in real-time, as well as on Frav3D and GavabDB databases. Our system attained a recognition rate of 95.83% after registration on frontal subjects using Haar wavelet as the feature extraction method, which depicts the robustness of the present system. Not only that, we have improved our recognition rate up to 96% using the Deep Convolutional Neural Network (DCNN).

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
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27

Similar content being viewed by others

Data availability

Authors confirm that they have no associated data with this manuscript.

References

  1. Achermann B, Jiang X, Bunke H (1997) Face recognition using range images. In Proceedings. International Conference on Virtual Systems and MultiMedia VSMM'97 (Cat. No. 97TB100182). IEEE, pp 129–136

  2. Alghaili M, Li Z and Ali HA (2020) “FaceFilter: Face Identification with Deep Learning and Filter Algorithm”, Sci Program

  3. Bagchi P, Bhattacharjee D, Nasipuri M, Basu DK (2012) A novel approach for registration of 3D face images. In IEEE-international conference on advances in engineering, science and management (ICAESM-2012) . IEEE, pp 1–7

  4. Bagchi P, Bhattacharjee D, Nasipuri M (2019) Reg3DFacePtCd: Registration of 3D Point Clouds Using a Common Set of Landmarks for Alignment of Human Face Images. Kunstl Intell 33:369–387. https://doi.org/10.1007/s13218-019-00593-2

    Article  Google Scholar 

  5. Ben AB (2008) “3D Face Recognition using ICP and Geodesic Computation Coupled Approach”, in Signal Processing for Image Enhancement and Multimedia Processing. Springer, Boston, MA, pp 141–151

    Google Scholar 

  6. Bhateja A, Shrivastav A, Chaudhary H, Lall B and Kalra PK (2021) “Depth analysis of kinect v2 sensor in different mediums”, Multimed Tools Appl 1–26

  7. Bhattacharjee D, Seal A, Ganguly S, Nasipuri M, Basu DK (2012) Comparative study of human thermal face recognition based on Haar wavelet transform and local binary pattern. Comput Intell Neurosci 2012:6–6

  8. Bhople AR, Akhilesh MS, Prakash S (2021) Point cloud based deep convolutional neural network for 3D face recognition. Multimed Tools Appl 80(20):30237–30259

    Article  Google Scholar 

  9. Bakkouri I, Afdel K (2019) Computer-aided diagnosis (CAD) system based on multi-layer feature fusion network for skin lesion recognition in dermoscopy images. Multim Tools Appl 79:20483–20518. https://doi.org/10.1007/s11042-019-07988-1

    Article  Google Scholar 

  10. Bakkouri I, Afdel K, Benois-Pineau J, Initiative GCFTASDN (2022) BG-3DM2F: Bidirectional gated 3D multi-scale feature fusion for Alzheimer’s disease diagnosis. Multim Tools Appl 81(8):10743–10776

  11. Doumanoglou A, Asteriadis S, Alexiadis DS, Zarpalas D, Daras P (2013) A dataset of Kinect-based 3D scans. In IVMSP 2013. IEEE,  pp 1–4

  12. Hu Z, Gui P, Feng Z, Zhao Q, Fu K, Liu F, Liu Z (2019) Boosting depth-based face recognition from a quality perspective. Sensors 19(19):4124

  13. Mahdi FP, Habib M, Ahad M, Rahman A, Mckeever S, Moslehuddin ASM, Vasant P (2017) Face recognition-based real-time system for surveillance. Intell Decis Technol 11(1):79–92

    Article  Google Scholar 

  14. Mayya V, Pai RM, Pai MM (2016) “Automatic facial expression recognition using DCNN”, Procedia Comput Sci

  15. Min R, Choi J, Medioni G and Dugelay JL (2012) “Real-time 3D face identification from a depth camera”, In Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), pp. 1739–1742. IEEE

  16. Moreno A (2004) “GavabDB: a 3D face database,” Proc. 2nd COST275 Work Biom Internet 2004:75–80

  17. Perakis P, Passalis G, Theoharis T, Kakadiaris I (2010) “ 3D facial landmark detection & face registration”, Techn Rep

  18. Ray B, Ghosh S, Ahmed S, Sarkar R, Nasipuri M (2022) Outlier detection using an ensemble of clustering algorithms. Multimed Tools Appl 81(2):2681–2709

    Article  Google Scholar 

  19. Regaya Y, Fadli F, Amira A (2021) Point-Denoise: Unsupervised outlier detection for 3D point clouds enhancement. Multim Tools Appl 80(18):28161–28177

  20. SalamaAbdELminaam D, Almansori AM, Taha M, Badr E (2020) A deep facial recognition system using computational intelligent algorithms. Plos One 15(12):e0242269

    Article  Google Scholar 

  21. Suchocki C, Bąk WB (2019) Down-sampling of point clouds for the technical diagnostics of buildings and structures. Geosciences 9(2):70

    Article  Google Scholar 

  22. Sui D, Hou D, Duan X (2017) An interpolation algorithm fitted for dynamic 3D face reconstruction. Multim Tools and Appl 76:19575–19589

  23. Su Y, Gao W, Liu Z, Sun S, Fu Y (2020) Hybrid marker-based object tracking using Kinect v2. IEEE Transactions on Instrumentation and Measurement 69(9):6436–6445

  24. Tulyakov S, Vieriu RL, Sangineto E, Sebe N (2015) Facecept3d: real time 3d face tracking and analysis. In Proceedings of the IEEE International Conference on Computer Vision Workshops, pp 28–33

  25. Yan P, Bowyer KW (2007) A fast algorithm for ICP-based 3D shape biometrics. Comput Vis Image Underst 107(3):195–202

    Article  Google Scholar 

  26. Zeng J, Qiu X, Shi S (2021) Image processing effects on the deep face recognition system. Math Biosci Eng 18(2):1187–1200

Download references

Author information

Authors and Affiliations

  1. RCC Institute of Information Technology, Kolkata, India

    Parama Bagchi

  2. Jadavpur University, Kolkata, India

    Debotosh Bhattacharjee

Authors
  1. Parama Bagchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Debotosh Bhattacharjee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Parama Bagchi.

Ethics declarations

Conflicts of interests/Competing interests

The authors hereby declare that they have conflicts of interests/competing interests.

Additional information

Publisher's note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bagchi, P., Bhattacharjee, D. JULive3D: a live image acquisition protocol for real-time 3D face recognition. Multimed Tools Appl 83, 1841–1868 (2024). https://doi.org/10.1007/s11042-023-15728-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-15728-9

Keywords

Search

Navigation