Skip to main content
SpringerLink
Log in

Face Presentation Attack Detection Using Remote Photoplethysmography Transformer Model

  • Conference paper
  • First Online:
Computer Vision and Image Processing (CVIP 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1777))

Included in the following conference series:

  • 429 Accesses

Abstract

Face Presentation Attack Detection (PAD) is essential for face recognition systems to achieve reliable verification in secured authentication applications. The face Presentation Attack Instruments include the printed photo, electronic display, wrap-photo and custom 3D masks. With the evolving technologies to generate the novel face PAI the generalisable PAD is of paramount importance. In this paper, we proposed a novel face PAD algorithm to achieve reliable detection of presentation attacks by quantifying the liveness using the remote photoplethysmography (rPPG) signal. The proposed method is developed by augmenting the PhysFormer model with an additional Temporal Difference Multi-Head Self-attention (TD-MHSA) block to obtain the reliable rPPG signal. We also proposed a novel classifier using 3DCNN to effectively capture the spatio-temporal to achieve a reliable PAD across different un-seen PAI. Extensive experiments are conducted on the publicly available OULU-NPU dataset comprised of four different PAI and six different smartphones. The proposed method is benchmarked with nine different existing PAD techniques on two different evaluation protocols and indicates considerable performance compared with the existing PAD techniques.

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 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
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. Abdullakutty, F., Elyan, E., Johnston, P.: A review of state-of-the-art in face presentation attack detection: from early development to advanced deep learning and multi-modal fusion methods. Inf. Fusion 75, 55–69 (2021)

    Article  Google Scholar 

  2. Boulkenafet, Z., et al.: A competition on generalized software-based face presentation attack detection in mobile scenarios. In: 2017 IEEE International Joint Conference on Biometrics (IJCB), pp. 688–696. IEEE (2017)

    Google Scholar 

  3. Boulkenafet, Z., Komulainen, J., Li, L., Feng, X., Hadid, A.: Oulu-npu: a mobile face presentation attack database with real-world variations. In: 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), pp. 612–618. IEEE (2017)

    Google Scholar 

  4. De Haan, G., Jeanne, V.: Robust pulse rate from chrominance-based rppg. IEEE Trans. Biomed. Eng. 60(10), 2878–2886 (2013)

    Article  Google Scholar 

  5. Fouad, R., Omer, O.A., Ali, A.M.M., Aly, M.H.: Refining roi selection for real-time remote photoplethysmography using adaptive skin detection

    Google Scholar 

  6. Galbally, J., Marcel, S., Fierrez, J.: Image quality assessment for fake biometric detection: application to iris, fingerprint, and face recognition. IEEE Trans. Image Process. 23(2), 710–724 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  7. George, A., Marcel, S.: Deep pixel-wise binary supervision for face presentation attack detection. In: 2019 International Conference on Biometrics (ICB), pp. 1–8. IEEE (2019)

    Google Scholar 

  8. Hernandez-Ortega, J., Fierrez, J., Morales, A., Galbally, J.: Introduction to face presentation attack detection. In: Marcel, S., Nixon, M.S., Fierrez, J., Evans, N. (eds.) Handbook of Biometric Anti-Spoofing. ACVPR, pp. 187–206. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-92627-8_9

    Chapter  Google Scholar 

  9. Hernandez-Ortega, J., Fierrez, J., Morales, A., Tome, P.: Time analysis of pulse-based face anti-spoofing in visible and nir. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 544–552 (2018)

    Google Scholar 

  10. Heusch, G., Marcel, S.: Pulse-based features for face presentation attack detection. In: 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS), pp. 1–8. IEEE (2018)

    Google Scholar 

  11. Hossain, M.S., Rupty, L., Roy, K., Hasan, M., Sengupta, S., Mohammed, N.: A-deeppixbis: attentional angular margin for face anti-spoofing. In: 2020 Digital Image Computing: Techniques and Applications (DICTA), pp. 1–8. IEEE (2020)

    Google Scholar 

  12. ISO/IEC JTC1 SC37 Biometrics: ISO/IEC 30107–1. Information Technology - Biometric presentation attack detection - Part 1: Framework. International Organization for Standardization (2016)

    Google Scholar 

  13. ISO/IEC JTC1 SC37 Biometrics: ISO/IEC 30107–3. Information Technology - Biometric presentation attack detection - Part 3: Testing and Reporting. International Organization for Standardization (2017)

    Google Scholar 

  14. King, D.: Dlib c library. http://dlib.net/

  15. Kossack, B., Wisotzky, E., Eisert, P., Schraven, S.P., Globke, B., Hilsmann, A.: Perfusion assessment via local remote photoplethysmography (rppg). In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 2192–2201 (June 2022)

    Google Scholar 

  16. Kossack, B., Wisotzky, E., Hilsmann, A., Eisert, P.: Automatic region-based heart rate measurement using remote photoplethysmography. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2755–2759 (2021)

    Google Scholar 

  17. Kossack, B., Wisotzky, E.L., Hilsmann, A., Eisert, P.: Local remote photoplethysmography signal analysis for application in presentation attack detection. In: VMV, pp. 135–142 (2019)

    Google Scholar 

  18. Kumar, M., Veeraraghavan, A., Sabharwal, A.: Distanceppg: robust non-contact vital signs monitoring using a camera. Biomed. Opt. Express 6(5), 1565–1588 (2015)

    Article  Google Scholar 

  19. Li, X., Chen, J., Zhao, G., Pietikainen, M.: Remote heart rate measurement from face videos under realistic situations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4264–4271 (2014)

    Google Scholar 

  20. Li, X., Komulainen, J., Zhao, G., Yuen, P.C., Pietikäinen, M.: Generalized face anti-spoofing by detecting pulse from face videos. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 4244–4249. IEEE (2016)

    Google Scholar 

  21. Lin, B., Li, X., Yu, Z., Zhao, G.: Face liveness detection by rppg features and contextual patch-based cnn. In: Proceedings of the 2019 3rd International Conference on Biometric Engineering and Applications, pp. 61–68 (2019)

    Google Scholar 

  22. Liu, S.-Q., Lan, X., Yuen, P.C.: Remote photoplethysmography correspondence feature for 3D mask face presentation attack detection. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11220, pp. 577–594. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01270-0_34

    Chapter  Google Scholar 

  23. Liu, S., Yuen, P.C., Zhang, S., Zhao, G.: 3D mask face anti-spoofing with remote photoplethysmography. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 85–100. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_6

    Chapter  Google Scholar 

  24. Liu, Y., Jourabloo, A., Liu, X.: Learning deep models for face anti-spoofing: binary or auxiliary supervision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 389–398 (2018)

    Google Scholar 

  25. McDuff, D.J., Estepp, J.R., Piasecki, A.M., Blackford, E.B.: A survey of remote optical photoplethysmographic imaging methods. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6398–6404. IEEE (2015)

    Google Scholar 

  26. Muckenhirn, H., Korshunov, P., Magimai-Doss, M., Marcel, S.: Long-term spectral statistics for voice presentation attack detection. IEEE/ACM Trans. Audio Speech Lang. Process. 25(11), 2098–2111 (2017)

    Article  Google Scholar 

  27. Muhammad, U., Yu, Z., Komulainen, J.: Self-supervised 2d face presentation attack detection via temporal sequence sampling. Pattern Recogn. Lett. 156, 15–22 (2022)

    Article  Google Scholar 

  28. Ni, A., Azarang, A., Kehtarnavaz, N.: A review of deep learning-based contactless heart rate measurement methods. Sensors 21(11), 3719 (2021)

    Article  Google Scholar 

  29. Niu, X., Shan, S., Han, H., Chen, X.: Rhythmnet: end-to-end heart rate estimation from face via spatial-temporal representation. IEEE Trans. Image Process. 29, 2409–2423 (2019)

    Article  MATH  Google Scholar 

  30. Niu, X., Yu, Z., Han, H., Li, X., Shan, S., Zhao, G.: Video-based remote physiological measurement via cross-verified feature disentangling. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12347, pp. 295–310. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58536-5_18

    Chapter  Google Scholar 

  31. Nowara, E.M., Sabharwal, A., Veeraraghavan, A.: Ppgsecure: biometric presentation attack detection using photopletysmograms. In: 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), pp. 56–62. IEEE (2017)

    Google Scholar 

  32. Poh, M.Z., McDuff, D.J., Picard, R.W.: Advancements in noncontact, multiparameter physiological measurements using a webcam. IEEE Trans. Biomed. Eng. 58(1), 7–11 (2010)

    Article  Google Scholar 

  33. Ramachandra, R., Busch, C.: Presentation attack detection methods for face recognition systems: a comprehensive survey. ACM Comput. Surv. (CSUR) 50(1), 1–37 (2017)

    Article  Google Scholar 

  34. Rouast, P.V., Adam, M.T., Chiong, R., Cornforth, D., Lux, E.: Remote heart rate measurement using low-cost rgb face video: a technical literature review. Front. Comp. Sci. 12(5), 858–872 (2018)

    Article  Google Scholar 

  35. Roy, K., et al.: Bi-fpnfas: Bi-directional feature pyramid network for pixel-wise face anti-spoofing by leveraging fourier spectra. Sensors 21(8), 2799 (2021)

    Article  Google Scholar 

  36. Suh, K.H., Lee, E.C.: Face liveness detection for face recognition based on cardiac features of skin color image. In: First International Workshop on Pattern Recognition, vol. 10011, pp. 62–66. SPIE (2016)

    Google Scholar 

  37. Verkruysse, W., Svaasand, L.O., Nelson, J.S.: Remote plethysmographic imaging using ambient light. Opt. Express 16(26), 21434–21445 (2008)

    Article  Google Scholar 

  38. Wang, W., Den Brinker, A.C., Stuijk, S., De Haan, G.: Algorithmic principles of remote ppg. IEEE Trans. Biomed. Eng. 64(7), 1479–1491 (2016)

    Article  Google Scholar 

  39. Yu, Z., Li, X., Wang, P., Zhao, G.: Transrppg: remote photoplethysmography transformer for 3d mask face presentation attack detection. IEEE Signal Process. Lett. 28, 1290–1294 (2021)

    Article  Google Scholar 

  40. Yu, Z., Shen, Y., Shi, J., Zhao, H., Torr, P.H., Zhao, G.: Physformer: facial video-based physiological measurement with temporal difference transformer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4186–4196 (2022)

    Google Scholar 

  41. Yu, Z., et al.: Searching central difference convolutional networks for face anti-spoofing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5295–5305 (2020)

    Google Scholar 

  42. 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 

Download references

Author information

Authors and Affiliations

  1. Norwegian University of Science and Technology, Teknologivegen 22, 2815, Gjøvik, Norway

    Haoyu Zhang, Raghavendra Ramachandra & Christoph Busch

  2. Darmstadt University of Applied Sciences, Schöfferstraße 3, 64295, Darmstadt, Germany

    Christoph Busch

Authors
  1. Haoyu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Raghavendra Ramachandra
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christoph Busch
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Haoyu Zhang .

Editor information

Editors and Affiliations

  1. Visvesvaraya National Institute of Technology Nagpur, Nagpur, India

    Deep Gupta

  2. Visvesvaraya National Institute of Technology Nagpur, Nagpur, India

    Kishor Bhurchandi

  3. Indian Institute of Technology Ropar, Rupnagar, India

    Subrahmanyam Murala

  4. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  5. Indian Institute of Technology Roorkee, Roorkee, India

    Sanjeev Kumar

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, H., Ramachandra, R., Busch, C. (2023). Face Presentation Attack Detection Using Remote Photoplethysmography Transformer Model. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1777. Springer, Cham. https://doi.org/10.1007/978-3-031-31417-9_42

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-31417-9_42

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-31416-2

  • Online ISBN: 978-3-031-31417-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation