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Anti-spoofing in face recognition-based biometric authentication using Image Quality Assessment

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Abstract

Despite the rapid growth of face recognition-based biometrics for both authentication and identification, the security of face biometric systems against presentation attacks (also called spoofing attacks) remains a great concern. Indeed, Face recognition-based authentication techniques can be easily spoofed using various types of attacks such photographs, videos or forged 3D masks. This work proposes a fast and non-intrusive anti-spoofing solution based on Image Quality Assessment (IQA) and motion cues to distinguish between genuine and fake face-appearances. Quality measures are computed following a novel approach which enables us to highlight these liveness-related motion cues, thus outlining the distinction between real faces and spoofing attacks. Moreover, our method is well suited for real-time mobile applications as it takes into consideration both reliable robustness and low complexity of employed algorithms. Our approach is extensively evaluated on three public databases that include different types of presentation attacks. The obtained results proved to outperform state-of-the-art approaches.

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References

  1. Akhtar Z, Foresti GL (2016) Face spoof attack recognition using discriminative image patches. Journal of Electrical and Computer Engineering 20

  2. Anjos A, Komulainen J, Marcel S, Hadid A, Pietikainen M (2014) Face anti-spoofing: Visual approach. In Sebastien Marcel, Mark Nixon, and Stan Z. Li, editors, Handbook of Biometric Anti-Spoofing, chapter 4, pages 65–82. Springer-Verlag

  3. Anjos A, Marcel S (2011) Counter-measures to photo attacks in face recognition: A public database and a baseline. In: 2011 International Joint Conference on Biometrics (IJCB), pp. 1–7

  4. Bao W, Li H, Li N, Jiang W (2009) A liveness detection method for face recognition based on optical flow field. In International Conference on Image Analysis and Signal Processing, pp. 233–236

  5. Barbu T, Ciobanu A, Luca M (2015) Multimodal biometric authentication based on voice, face and iris. In 2015 E-Health and Bioengineering Conference (EHB), pp. 1–4

  6. Bharadwaj S, Dhamecha T, Vatsa M, Singh R (2013) Computationally efficient face spoofing detection with motion magnification. In IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 105–110

  7. Boulkenafet Z, Komulainen J, Hadid A (2015) Face anti-spoofing based on color texture analysis. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 2636–2640

  8. Cheng HT, Chao YH, Yeh SL, Chen CS, Wang HM, Hung YP (2005) An efficient approach to multimodal person identity verification by fusing face and voice information. In: 2005 IEEE International Conference on Multimedia and Expo, pp. 542–545

  9. Chingovska I, Anjos A, Marcel S (2012) On the effectiveness of local binary patterns in face anti-spoofing. In 2012 BIOSIG - Proceedings of the InternationalConference of Biometrics Special Interest Group (BIOSIG), pp. 1–7

  10. CostaPazo A, Bhattacharjee S, VazquezFernandez E, Marcel S (2016) The replaymobile face presentation-attack database. In 2016 International Conference of the Biometrics Special Interest Group (BIOSIG), pp. 1–7

  11. de Freitas-Pereira T, Anjos A, De Martino JM, Marcel S (2013) Can face antispoofing countermeasures work in a real world scenario? In 2013 International Conference on Biometrics (ICB), pp. 1–8

  12. Dominik S, Pauline T, Andreas U (2018) Non-reference Image Quality Assessment and Natural Scene Statisticsto Counter Biometric Sensor Spoofing. IET Biometrics. https://doi.org/10.1049/iet-bmt.2017.0146

    Article  Google Scholar 

  13. Erdogmus N, Marcel S (2013) Spoofing in 2d face recognition with 3d masks and anti-spoofing with kinect. In Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)

  14. Feng L, Po LM, Li Y, Xu X, Yuan F (2016) T.C.H. Cheung, K.W. Cheung. Integration of image quality and motion cues for face anti-spoofing: A neural network approach. Journal of Visual Communication and Image Representation R 38:451–460

    Article  Google Scholar 

  15. Fourati E, Elloumi W, Chetouani A (2017) Face anti-spoofing with image quality assessment. In: 2017 2nd International Conference on Bio-engineering for Smart Technologies (BioSMART)

  16. Galbally J, Marcel S, Fierrez J (2014) Image quality assessment for fake biometric detection: Application to iris, fingerprint, and face recognition. IEEE Trans Image Process 23(2):710–724

    Article  MathSciNet  Google Scholar 

  17. Ghadiyaram D, Bovik AC (2016) Perceptual quality prediction on authentically distorted images using a bag of features approach. J Vis

  18. Hastie T, Tibshirani R, Friedman J (2001) The Elements of Statistical Learning. In Springer Series in Statistics. Springer New York Inc., New York

  19. Jee HK, Jung SU, Yoo JH (2008) Liveness detection for embedded face recognition system. International Journal of Computer, Electrical, Automation, Control and Information Engineering 2(6):2142–2145

    Google Scholar 

  20. Kollreider K, Fronthaler H, Bigun J (2009) Non-intrusive liveness detection by face images. In Image and Vision Computing

  21. Kollreider K, Fronthaler H, Faraj MI, Bigun J (2007) Real-time face detection and motion analysis with application in liveness assessment. IEEE Transaction on Information Forensics and Security 2(3):548–558

    Article  Google Scholar 

  22. Komulainen J, Anina I, Holappa J, Boutellaa E, Hadid A (2016) On the robustness of audiovisual liveness detection to visual speech animation. In 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS), pp. 1–8

  23. Kundu D, Ghadiyaram D, Bovik AC, Evans BL (2016) No-reference image quality assessment for high dynamic range images. In: 2016 50th Asilomar Conference on Signals, Systems and Computers, pages 1847–1852

  24. Lagorio A, Tistarelli M, Cadoni M, Fookes C, Sridharan S (2013) Liveness detection based on 3d face shape analysis. In International Workshop on Biometrics and Forensics (IWBF), pp. 1–4

  25. Lee PH, Chu LJ, Hung YP, Shih SW, Chen CS, Wang HM (2007) Cascading multimodal verification using face, voice and iris information. In: 2007 IEEE International Conference on Multimedia and Expo, pp. 847–850

  26. Li L, Feng X, Boulkenafet Z, Xia Z, Li M, Hadid A (2016) An original face anti-spoofing approach using partial convolutional neural network. In 6th International Conference on In Image Processing Theory Tools and Applications (IPTA), Oulu, Finland, pp. 1–6. 10.1109/IPTA.2016.7821013

  27. Liu L, Dong H, Huang H, Bovik AC (2014) No-reference image quality assessment in curvelet domain. Signal Process Image Commun 29:494–505

    Article  Google Scholar 

  28. Lucena O, Junior A, Hugo V, Moia G, Souza R, Valle E, De Alencar Lotufo R (2017) Transfer learning using convolutional neural networks for face anti-spoofing. In book: Image Analysis and Recognition, pp. 27-34

    Google Scholar 

  29. Manjani I, Tariyal S, Vatsa M, Singh R, Majumdar A (2017) Detecting silicone mask based presentation attack via deep dictionary learning. IEEE Transactions on Information Forensics and Security 12(7):1713–1723. https://doi.org/10.1109/TIFS.2017.2676720

    Article  Google Scholar 

  30. Melnikov A, Akhunzyanov R, Oleg K, Luckyanets E (2015) Audiovisual liveness detection. In book: Counting Turkish Coins with a Calibrated Camera, 9280:643–652

    Chapter  Google Scholar 

  31. Mittal A, Moorthy AK, Bovik AC (2012) No-reference image quality assessment in the spatial domain. IEEE Trans Image Process 21(12):4695–4708

    Article  MathSciNet  Google Scholar 

  32. Mittal A, Moorthy AK, Bovik AC (2012) BRISQUE Software Release. http://live.ece.utexas.edu/research/quality/BRISQUE_release.zip

  33. Mittal A, Moorthy AK, Bovik AC (2012) Making image quality assessment robust. In 2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), pp. 1718–1722

  34. Mittal A, Soundararajan R, Bovik AC (2012) NIQE Software Release. http://live.ece.utexas.edu/research/quality/niqe.zip

  35. Mittal A, Soundararajan R, Bovik AC (2013) Making a completely blind image quality analyzer. IEEE Signal Processing Letters 20:209–212

    Article  Google Scholar 

  36. Moorthy AK, Bovik AC (2009) BIQI Software Release. http://live.ece.utexas.edu/research/quality/biqi.zip

  37. Moorthy AK, Bovik AC (2009) A modular framework for constructing blind universal quality indices. IEEE Signal Processing Letters

  38. Moorthy AK, Bovik AC (2011) Blind image quality assessment: From natural scene statistics to perceptual quality. IEEE Trans Image Process 20(12):3350–3364

    Article  MathSciNet  Google Scholar 

  39. Ng ES, Chia AYS (2012) Face verification using temporal affective cues. In: International Conference on Pattern Recognition (ICPR), pp. 1249–1252

  40. Pan G, Wu Z, Sun L (2008) Recent advances in face recognition. In Liveness Detection for Face Recognition, pp. 109-124

    Google Scholar 

  41. Peng H, Long F, Ding C (2005) Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238

    Article  Google Scholar 

  42. Raghavendra R, Raja KB, Venkatesh S, Busch C (2017) Transferable deep-CNN features for detecting digital and print-scanned morphed face images. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1822-1830

  43. Ruderman DL (1994) The statistics of natural images. Netw Comput Neural Syst 5(4):517–548

    Article  Google Scholar 

  44. Tian Y, Xiang S (2017) Detection of video-based face spoofing using lbp and multiscale DCT. In: Shi Y, Kim H, Perez-Gonzalez F, Liu F (eds) Digital Forensics and Watermarking. IWDW 2016. Lecture Notes in Computer Science, vol 10082. Springer, Cham

    Google Scholar 

  45. Ur Rehman YA, Lai Man P, Liu M (2018) LiveNet: Improving Features Generalization for Face Liveness Detection using Convolution Neural Networks. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2018.05.004

    Article  Google Scholar 

  46. Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, volume 1, pages I–511–I–518 vol. 1

  47. Wang T, Yang J, Lei Z, Liao S, Li SZ (2013) Face liveness detection using 3d structure recovered from a single camera. In 2013 International Conference on Biometrics (ICB), pp. 1–6

  48. Xue W, Mou X, Zhang L, Bovik AC, Feng X (2014) Blind image quality assessment using joint statistics of gradient magnitude and laplacian features. IEEE Trans Image Process 23(11):4850–4862

    Article  MathSciNet  Google Scholar 

  49. Zhang L, Zhang L, Bovik AC (2015) A feature-enriched completely blind image quality evaluator. IEEE Trans Image Process 24(8):2579–2591

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Worldline, 19 Rue de la Vallée Maillard, Blois, France

    Emna Fourati & Wael Elloumi

  2. Laboratoire PRISME, Université d’Orléans, 12 rue de Blois, Orléans, France

    Aladine Chetouani

Authors
  1. Emna Fourati
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  2. Wael Elloumi
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  3. Aladine Chetouani
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Correspondence to Wael Elloumi.

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Fourati, E., Elloumi, W. & Chetouani, A. Anti-spoofing in face recognition-based biometric authentication using Image Quality Assessment. Multimed Tools Appl 79, 865–889 (2020). https://doi.org/10.1007/s11042-019-08115-w

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