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A Contemporary Survey of Multimodal Presentation Attack Detection Techniques: Challenges and Opportunities

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Abstract

Biometric recognition is a broad and dynamic field of research but the main problem of this field is spoofing attack or presentation attack “use of fake biometric in place of the real biometric sample from original user”. Liveness detection is the prime countermeasure to spoofing attacks, which is based on the principle that some additional information can be obtained to verify that the produced data is genuine or not by a standard verification system. It utilized anatomical signs of life, such as facial expression, blinking of eyes, movement of the head, etc. This paper presents a comprehensive review of various liveness detection techniques based on a multimodal biometric system, in which physiological and behavioral properties are used to differentiate between genuine and fake biometric traits. Multimodal systems utilize two or more biometric traits which makes them more secure as compared to unimodal systems. These systems overcome the limitations of the unimodal system such as spoof attack, noisy data, non-universality, distinctiveness, and intra-class variations, etc. Hence to make the biometric systems more secure and robust, multimodal techniques are used. In this paper, we categorized and discuss the various multimodal biometric techniques proposed by various researchers in the last decade, and a new classification is also developed for the same. This paper covers theories, methodology, evaluation datasets, and aims at future work in this field of research.

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References

  1. Hadid A, Evans N, Marcel S, Fierrez J. Biometrics systems under spoofing attack: an evaluation methodology and lessons learned. IEEE Signal Process Mag. 2015;32:20–30.

    Article  Google Scholar 

  2. Akhtar Z, Micheloni C, Foresti GL. Biometric liveness detection: challenges and research opportunities. IEEE Secu Priv. 2015;13:63–72.

    Article  Google Scholar 

  3. Marcel S, Nixon MS, Li SZ. Handbook of biometric anti-spoofing. London: Springer London; 2014. p. 1–279.

    Google Scholar 

  4. Shahin MK, Badawi AM, Rasmy ME. A multimodal hand vein, hand geometry, and fingerprint prototype design for high security biometrics. In: Proceedings of the 2008 Cairo international biomedical engineering conference. IEEE; 2008. p. 1–6.

  5. Rodrigues RN, Ling LL, Govindaraju V. Robustness of multimodal biometric fusion methods against spoof attacks. J Vis Lang Comput. 2009;20:169–79.

    Article  Google Scholar 

  6. Jiang RM, Sadka AH, Crookes D. Multimodal biometric human recognition for perceptual human–computer interaction. IEEE Trans Syst Man Cybern Part C Appl Rev. 2010;40:676–81.

    Article  Google Scholar 

  7. Gomez-Barrero M, Galbally J, Fierrez J. Efficient software attack to multimodal biometric systems and its application to face and iris fusion. Pattern Recognit Lett. 2014;36:243–53.

    Article  Google Scholar 

  8. Das A, Pal U, Ferrer MA, Blumenstein M. A framework for liveness detection for direct attacks in the visible spectrum for multimodal ocular biometrics. Pattern Recognit Lett. 2016;82:232–41.

    Article  Google Scholar 

  9. Kavitha P, Vijaya K. Optimal feature-level fusion and layered k-support vector machine for spoofing face detection. Multimed Tools Appl. 2018;77:26509–43.

    Article  Google Scholar 

  10. Jain AK, Hong L, Kulkarni Y. A multimodal biometric system using fingerprint, face, and speech 1999; 10.

  11. Komeili M, Armanfard N, Hatzinakos D. Liveness detection and automatic template updating using fusion of ecg and fingerprint. IEEE Trans Inf Forensics Secur. 2018;13:1810–22.

    Article  Google Scholar 

  12. Walia GS, Singh T, Singh K, Verma N. Robust multimodal biometric system based on optimal score level fusion model. Expert Syst Appl. 2019;116:364–76.

    Article  Google Scholar 

  13. Chetty G, Wagner M. Multi-level liveness verification for face-voice biometric authentication. In: Proceedings of the 2006 Biometrics Symposium: special session on research at the biometric consortium conference. IEEE; 2006. p. 1–6.

  14. Akhtar, Z., Micheloni, C., Piciarelli, C., Foresti, G.L.: MoBio; LivDet: Mobile biometric liveness detection. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). pp. 187–192. IEEE (2014)

  15. Gupta K, Walia GS, Sharma K. Multimodal biometric system using grasshopper optimization. In: 2019 international conference on computing, communication, and intelligent systems (ICCCIS); 2019. IEEE, p. 387–391.

  16. Bao W, Li H, Li N, Jiang W. A liveness detection method for face recognition based on optical flow field. In: Proceedings of 2009 international conference image analysing signal process; 2009. IASP 2009, p. 233–236.

  17. Sahidullah M, Thomsen DAL, Hautamaki RG, Kinnunen T, Tan ZH, Parts R, Pitkanen M. Robust voice liveness detection and speaker verification using throat microphones. IEEE/ACM Trans Audio Speech Lang Process. 2018;26:44–56.

    Article  Google Scholar 

  18. Kim W, Hong W, Kim T, Kim D, Lee M. RF sensor-based liveness detection scheme with loop stability compensation circuit for a capacitive fingerprint system. IEEE Access. 2019;7:152545–51.

    Article  Google Scholar 

  19. Albakri G, Alghowinem S. The effectiveness of depth data in liveness face authentication using 3D sensor cameras. Sensors. 2019;19:1928.

    Article  Google Scholar 

  20. Wang, Y., Cai, W., Gu, T., Shao, W., Li, Y., Yu, Y.: Secure Your Voice. In: Proceedings of ACM interactive, mobile, wearable ubiquitous technology, vol 3. 2019. p. 1–28.

  21. Ma Li, Tan T, Wang Y, Zhang D. Personal identification based on iris texture analysis. IEEE Trans Pattern Anal Mach Intell. 2003;25:1519–33.

    Article  Google Scholar 

  22. Chen R, Lin X, Ding T. Liveness detection for iris recognition using multispectral images. Pattern Recognit Lett. 2012;33:1513–9.

    Article  Google Scholar 

  23. Parveen S, Ahmad S, Abbas N, Adnan W, Hanafi M, Naeem N. Face liveness detection using dynamic local ternary pattern (DLTP). Computers. 2016;5:10.

    Article  Google Scholar 

  24. Boulkenafet Z, Komulainen J, Hadid A. Face spoofing detection using colour texture analysis. IEEE Trans Inf Forensics Secur. 2016;11:1818–30.

    Article  Google Scholar 

  25. Agarwal R, Jalal AS, Arya K. A multimodal liveness detection using statistical texture features and spatial analysis. Multimed Tools Appl. 2020;79:13621–13645.

    Article  Google Scholar 

  26. Sun L, Pan G, Wu Z, Lao S. Blinking-based live face detection using conditional random fields. In: Advances in Biometrics. vol. 4642 LNCS. Springer: Berlin, Heidelberg; 2007. p. 252–260

  27. Zhao G, Pietik M. Patterns with an application to facial expressions. Most. 2007;29:1–14.

    Google Scholar 

  28. Wang L, Ding X, Fang C. Face live detection method based on physiological motion analysis. Tsinghua Sci Technol. 2009;14:685–90.

    Article  Google Scholar 

  29. Chetty G. Biometric liveness checking using multimodal fuzzy fusion. In: 2010 IEEE world congress computational intelligence; 2010. WCCI, p. 1–8.

  30. Komogortsev OV, Karpov A. Liveness detection via oculomotor plant characteristics: attack of mechanical replicas. In: Proceedings of 2013 international conference biology; 2013. ICB.

  31. Singh AK, Joshi P, Nandi GC. Face recognition with liveness detection using eye and mouth movement. In: 2014 international conference on signal propagation and computer technology (ICSPCT 2014); 2014. IEEE, p. 592–597.

  32. Somasundaram G, Cherian A, Morellas V, Papanikolopoulos N. Action recognition using global spatio-temporal features derived from sparse representations. Comput Vis Image Underst. 2014;123:1–13.

    Article  Google Scholar 

  33. Nagrani A, Albanie S, Zisserman A. Seeing voices and hearing faces: cross-modal biometric matching. In: 2018 IEEE/CVF conference on computer vision and pattern recognition; 2018. IEEE, p. 8427–8436.

  34. Chen FM, Wen C, Xie K, Wen FQ, Sheng GQ, Tang XG. Face liveness detection: fusing colour texture feature and deep feature. IET Biom. 2019;8:369–77.

    Article  Google Scholar 

  35. Schardosim LR, Dos Santos RR, Scharcanski J. Detection of presentation attacks using imaging and liveness attributes. Electron Lett. 2019;55:1226–9.

    Article  Google Scholar 

  36. Saad MA, Bovik AC, Charrier C. Blind image quality assessment: a natural scene statistics approach in the DCT domain. IEEE Trans Image Process. 2012;21:3339–52.

    Article  MathSciNet  Google Scholar 

  37. Lee YH, Khalil-Hani M, Bakhteri R, Nambiar VP. A real-time near infrared image acquisition system based on image quality assessment. J Real Time Image Process. 2017;13:103–20.

    Article  Google Scholar 

  38. Akhtar Z, Foresti GL. Face spoof attack recognition using discriminative image patches. J Electr Comput Eng. 2016;2016:1–15.

    Article  Google Scholar 

  39. Xia Z, Lv R, Sun X. Rotation-invariant Weber pattern and Gabor feature for fingerprint liveness detection. Multimed Tools Appl. 2018;77:18187–200.

    Article  Google Scholar 

  40. Söllinger D, Trung P, Uhl A. Non-reference image quality assessment and natural scene statistics to counter biometric sensor spoofing. IET Biom. 2018;7:314–24.

    Article  Google Scholar 

  41. Singh K, Vishwakarma DK, Walia GS. Blind image deblurring via gradient orientation-based clustered coupled sparse dictionaries. Pattern Anal Appl. 2019;22:549–58.

    Article  MathSciNet  Google Scholar 

  42. Wild P, Radu P, Chen L, Ferryman J. Robust multimodal face and fingerprint fusion in the presence of spoofing attacks. Pattern Recognit. 2016;50:17–25.

    Article  Google Scholar 

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This article is part of the topical collection “Advances in Computational Approaches for Artificial Intelligence, Image Processing, IoT and Cloud Applications” guest edited by Bhanu Prakash K N and M. Shivakumar.

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Kavita, Walia, G.S. & Rohilla, R. A Contemporary Survey of Multimodal Presentation Attack Detection Techniques: Challenges and Opportunities. SN COMPUT. SCI. 2, 49 (2021). https://doi.org/10.1007/s42979-020-00425-3

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