Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon openaccess Non-reference image quality assessment and natural scene statistics to counter biometric sensor spoofing

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 15/08/2017, Accepted 13/12/2017, Revised 28/11/2017, Published 22/01/2018

Non-reference image quality measures (IQM) as well as their associated natural scene statistics (NSS) are used to distinguish real biometric data from fake data as used in presentation/sensor spoofing attacks. An experimental study shows that a support vector machine directly trained on NSS as used in blind/referenceless image spatial quality evaluator provides highly accurate classification of real versus fake iris, fingerprint, face, and fingervein data in generic manner. This contrasts to using the IQM directly, the accuracy of which turns out to be rather data set and parameter choice-dependent. While providing very low average classification error rate values for complete training data, generalisation to unseen attack types is difficult in open-set scenarios and obtained accuracy varies in almost unpredictable manner. This implies that for each given sensor/attack set-up, the ability of the introduced methods to detect unseen attacks needs to be assessed separately.

Inspec keywords: natural scenes; fingerprint identification; biometrics (access control); image processing; support vector machines; pattern classification; learning (artificial intelligence); statistical analysis; feature extraction; iris recognition

Other keywords: open-set scenarios; associated natural scene statistics; NSS; IQM; biometric sensor spoofing; complete training data; biometric data; experimental study; blind/referenceless image spatial quality evaluator; presentation/sensor spoofing attacks; unseen attacks; generic manner; low average classification error rate values; highly accurate classification; given sensor/attack; fake data; unseen attack types; versus fake iris; support vector machine; nonreference image quality assessment; nonreference image quality measures; fingervein data

Subjects: Computer vision and image processing techniques; Optical, image and video signal processing; Knowledge engineering techniques; Other topics in statistics; Other topics in statistics

References

    1. 1)
      • 20. Bhardwaj, I., Londhe, N.D., Kopparau, S.K.: ‘A spoof resistant multibiometric system based on the physiological and behavioral characteristics of fingerprint’, Pattern Recognit., 2017, 62, pp. 214–224.
    2. 2)
      • 16. Moorthy, A.K., Bovik, A.C.: ‘A two-step framework for constructing blind image quality indices’, IEEE Signal Process. Lett., 2010, 17, (5), pp. 513–516.
    3. 3)
      • 37. Marasco, E., Wild, P., Cukic, B.: ‘Robust and interoperable fingerprint spoof detection via convolutional neural networks’. Proc. of the IEEE Symp. on Technologies for Homeland Security (HST'16), May 2016, pp. 1–6.
    4. 4)
      • 41. Doyle, J.S., Bowyer, K.W., Flynn, P.J.: ‘Variation in accuracy of textured contact lens detection based on sensor and lens pattern’. Proc. of the Int. Conf. on Biometrics: Theory, Applications and Systems (BTAS'13), September 2013, pp. 1–7.
    5. 5)
      • 46. Ton, B.T., Veldhuis, R.N.J.: ‘A high quality finger vascular pattern dataset collected using a custom designed capturing device’. Int. Conf. on Biometrics, ICB 2013. IEEE, 2013.
    6. 6)
      • 24. Tirunagari, S., Poh, N., Bober, M., et al: ‘Windowed DMD as a microtexture descriptor for finger vein counter-spoofing in biometrics’. 2015 IEEE Int. Workshop on Information Forensics and Security (WIFS), November 2015, pp. 1–6.
    7. 7)
      • 43. Yin, Y., Liu, L., Sun, X.: ‘SDUMLA-HMT: A multimodal biometric database’. The 6th Chinese Conf. on Biometric Recognition (CCBR 2011), volume 7098 of Springer Lecture Notes on Computer Science, 2011, pp. 260–268.
    8. 8)
      • 9. Nouri, A., Charrier, C., Saadane, A., et al: ‘Statistical comparison of no-reference images quality assessment algorithms’. Proc. of the Colour and Visual Computing Symp. (CVCS'13), 2013.
    9. 9)
      • 5. Galbally, J., Marcel, S., Fierrez, J.: ‘Image quality assessment for fake biometric detection: application to iris, fingerprint, and face recognition’, IEEE Trans. Image Process., 2014, 23, (2), pp. 710–724.
    10. 10)
      • 12. Saad, M., Bovik, A.C., Charrier, C.: ‘Blind image quality assessment: a natural scene statistics approach in the DCT domain’, IEEE Trans. Image Process., 2012, 21, (8), pp. 3339–3352.
    11. 11)
      • 40. Korshunov, P., Marcel, S.: ‘Cross-database evaluation of audio-based spoofing detection systems’. Proc. of the Annual Conf. of the Int. Speech Communication Association, 2016, pp. 1705–1709.
    12. 12)
      • 22. Chingovska, I., Anjos, A., Marcel, S.: ‘On the effectiveness of local binary patterns in face anti-spoofing’. Proc. of the Int. Conf. of the Biometrics Special Interest Group (BIOSIG'16), September 2012.
    13. 13)
      • 42. Czajka, A., Bowyer, K.W., Krumdick, M., et al: ‘Recognition of image-orientation-based iris spoofing’, IEEE Trans. Inf. Forensics Sec., 2017, 12, (9), pp. 2184–2196.
    14. 14)
      • 45. Kirchgasser, S., Uhl, A.: ‘Template ageing in non-minutiae fingerprint recognition’. Proc. of the Int. Workshop on Biometrics and Forensics (IWBF ‘17), Coventry, UK, 2017, pp. 1–6.
    15. 15)
      • 11. Mittal, A., Soundararajan, R., Bovik, A.C.: ‘Making image quality assessment robust’. Proceedings of the 46th Asilomar Conf. on Signals, Systems and Computers (ASILOMAR), 2012.
    16. 16)
      • 13. Gabarda, S., Cristobal, G.: ‘Blind image quality assessment through anisotropy’, J. Opt. Soc. Am. A, 2007, 24, pp. 24–51.
    17. 17)
      • 26. Kocher, D., Schwarz, S., Uhl, A.: ‘Empirical evaluation of LBP-extension features for finger vein spoofing detection’. Proc. of the Int. Conf. of the Biometrics Special Interest Group (BIOSIG'16), Darmstadt, Germany, 2016, p. 8.
    18. 18)
      • 21. Lu, M., Chen, Z., Sheng, W.: ‘Fingerprint liveness detection based on pore analysis’. Biometric Recognition – Proc. of the Chinese Conf. on Biometric Recognition (CCBR'15, Springer LNCS, 9428), 2015, pp. 233–240.
    19. 19)
      • 7. Wen, D., Han, H., Jain, A.K.: ‘Face spoof detection with image distortion analysis’, IEEE Trans. Inf. Forensics Sec., 2015, 10, (4), pp. 746–761.
    20. 20)
      • 35. Costa, F.O., Silva, E., Eckmann, M., et al: ‘Open set source camera attribution and device linking’, Pattern Recognit. Lett., 2014, 36, pp. 92–101.
    21. 21)
      • 6. Bhogal, A.P.S, Söllinger, D., Trung, P., et al: ‘Non-reference image quality assessment for biometric presentation attack detection (best reviewed papers session)’. Proc. of the 5th Int. Workshop on Biometrics and Forensics (IWBF'17), Coventry, UK, 2017, pp. 1–6.
    22. 22)
      • 33. Jain, L.P., Scheirer, W.J., Boult, T.E.: ‘Multi-class open set recognition using probability of inclusion’. The European Conf. on Computer Vision (ECCV), September 2014.
    23. 23)
      • 23. Tome, P., Raghavendra, R., Busch, C., et al: ‘The 1st competition on counter measures to finger vein spoofing attacks’. 2015 Int. Conf. on Biometrics (ICB), May 2015, pp. 513–518.
    24. 24)
      • 30. Guan, Y., Wei, X., Li, C.-T.: ‘On the generalization power of face and gait in gender recognition’, Int. J. Digit. Crime Forensics, 2014, 6, (1), pp. 1–8.
    25. 25)
      • 10. Charrier, C., Saadane, A., Fernandez-Maloigne, C.: ‘Comparison of no-reference image quality assessment machine learning-based algorithms on compressed images’. Image Quality and System Performance XII, volume 9396 of Proceedings of SPIE, 2015.
    26. 26)
      • 36. Akhtar, Z., Micheloni, C., Foresti, G.L.: ‘Correlation based fingerprint liveness detection’. Proc. of the Int. Conf. on Biometrics (ICB'15), May 2015, pp. 305–310.
    27. 27)
      • 39. Patel, K, Han, H., Jain, A.K.: ‘Cross-database face antispoofing with robust feature representation’. Proc. of the 11th Chinese Conf. on Biometric Recognition (CCBR'16), 2016, pp. 611–619.
    28. 28)
      • 34. Heflin, B., Scheirer, W.J., Boult, T.E.: ‘Detecting and classifying scars, marks, and tattoos found in the wild’. The IEEE Int. Conf. on Biometrics: Theory, Applications and Systems (BTAS), September 2012.
    29. 29)
      • 17. Fierrez, J., Ortega-Garcia, J., Torre-Toledano, D., et al: ‘Biosec baseline corpus: a multimodal biometric database’, Pattern Recognit., 2007, 40, (4), pp. 1389–1392.
    30. 30)
      • 1. Marcel, S., Nixon, M.S., Li, S.Z. (Eds.): ‘Handbook of biometric anti-spoofing’ (Springer, London, 2014).
    31. 31)
      • 27. Chang, C.-C., Lin, C.-J.: ‘LIBSVM: a library for support vector machines’, ACM Trans. Intell. Syst. Technol., 2011, 2, (27), pp. 1–27. Available at http://www.csie.ntu.edu.tw/cjlin/libsvm.
    32. 32)
      • 31. Scheirer, W.J., Rocha, A., Sapkota, A., et al: ‘Towards open set recognition’, IEEE Trans. Pattern Anal. Mach. Intell. (T-PAMI), 2013, 35, pp. 1757–1772.
    33. 33)
      • 2. Gragnaniello, D., Poggi, G., Sansone, C., et al: ‘An investigation of local descriptors for biometric spoofing detection’, IEEE Trans. Inf. Forensics Sec., 2015, 10, (4), pp. 849–861.
    34. 34)
      • 8. Bhogal, A.P.S, Söllinger, D., Trung, P., et al: ‘Non-reference image quality assessment for fingervein presentation attack detection’. Proc. of 20th Scandinavian Conf. on Image Analysis (SCIA'17)(Springer Lecture Notes on Computer Science, 10269), 2017, pp. 184–196.
    35. 35)
      • 44. Kirchgasser, S., Uhl, A.: ‘Template ageing and quality analysis in time-span separated fingerprint data’. Proc. of the IEEE Int. Conf. on Identity, Security and Behavior Analysis (ISBA ‘17), New Delhi, India, 2017, pp. 1–8.
    36. 36)
      • 4. Raghavendra, R., Raja, K., Venkatesh, S., et al: ‘Transferable deep convolutional neural network features for fingervein presentation attack detection’. Proc. of the 5th Int. Workshop on Biometrics and Forensics (IWBF'17), Coventry, UK, 2017, pp. 1–6.
    37. 37)
      • 19. Galbally, J., Alonso-Fernandez, F., Fierrez, J., et al: ‘A high performance fingerprint liveness detection method based on quality related features’, Future Gener. Comput. Syst., 2012, 28, pp. 311–321.
    38. 38)
      • 3. Menotti, D., Chiachia, G., Pinto, A., et al: ‘Deep representations for iris, face, and fingerprint spoofing detection’, IEEE Trans. Inf. Forensics Sec., 2015, 10, (4), pp. 864–879.
    39. 39)
      • 32. Scheirer, W.J., Jain, L.P., Boult, T.E.: ‘Probability models for open set recognition’, IEEE Trans. Pattern Anal. Mach. Intell. (T-PAMI), 2014, 36, pp. 2317–2324.
    40. 40)
      • 15. Moorthy, A.K., Bovik, A.C.: ‘Blind image quality assessment: from natural scene statistics to perceptual quality’, IEEE Trans. Image Process., 2011, 20, (12), pp. 3350–3364.
    41. 41)
      • 18. Galbally, J., Ortiz-Lopez, J., Fierrez, J., et al: ‘Iris liveness detection based on quality related features’. Proc. of the IAPR/IEEE Int. Conf. on Biometrics (ICB'12), March 2012, pp. 271–276.
    42. 42)
      • 38. Chugh, T., Cao, K., Jain, A.K.: ‘Fingerprint spoof detection using minutiae-based local patches’. Proc. of the Int. Joint Conf. on Biometrics (IJCB'17), 2017.
    43. 43)
      • 29. Torralba, A., Efros, A.: ‘Unbiased look at dataset bias’. Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR'11), 2011.
    44. 44)
      • 28. Rattani, A., Scheirer, W.J., Ross, A.: ‘Open set fingerprint spoof detection across novel fabrication materials’, IEEE Trans. Inf. Forensics Sec. (T-IFS), 2015, 10, pp. 2447–2460.
    45. 45)
      • 25. Raghavendra, R., Busch, C.: ‘Presentation attack detection algorithms for finger vein biometrics: a comprehensive study’. 2015 11th Int. Conf. on Signal-Image Technology Internet-Based Systems (SITIS), November 2015, pp. 628–632.
    46. 46)
      • 14. Mittal, A., Moorthy, A.K., Bovik, A.C.: ‘No-reference image quality assessment in the spatial domain’, IEEE Trans. Image Process., 2012, 21, (12), pp. 4695–4708.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-bmt.2017.0146
Loading

Related content

content/journals/10.1049/iet-bmt.2017.0146
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address