Face Presentation Attack Detection using Biologically-inspired Features

Aristeidis Tsitiridis, Cristina Conde, Isaac Martín De Diego, Enrique Cabello

Abstract

A person intentionally concealing or faking their identity from biometric security systems is known to perform a ‘presentation attack’. Efficient presentation attack detection poses a challenging problem in modern biometric security systems. Sophisticated presentation attacks may successfully spoof a person’s face and therefore, disrupt accurate biometric authentication in controlled areas. In this work, a presentation attack detection technique which processes biologically-inspired facial features is introduced. The main goal of the proposed method is to provide an alternative foundation for biometric detection systems. In addition, such a system can be used for future generation biometric systems capable of carrying out rapid facial perception tasks in complex and dynamic situations. The newly-developed model was tested against two different databases and classifiers. Presentation attack detection results have shown promise, exceeding 94% detection accuracy on average for the investigated databases. The proposed model can be enriched with future enhancements that can further improve its effectiveness and complexity in more diverse situations and sophisticated attacks in the real world.

References

  1. Anjos, A., Chakka, M.M. & Marcel, S., 2014. Motionbased counter-measures to photo attacks in face recognition. IET Biometrics, 3(3), pp.147-158.
  2. Axelrod, V. & Yovel, G., 2012. Hierarchical Processing of Face Viewpoint in Human Visual Cortex. Journal of Neuroscience, 32, pp.2442-2452.
  3. Chakraborty, S. & Das, D., 2014. An overview of Face Liveness Detection. International journal of Information Theory, 3(2), pp.11-25.
  4. Chingovska, I., Anjos, A. & Marcel, E., 2012. On the effectiveness of local binary patterns in face antispoofing. International Conference of the Biometrics Special Interest Group.
  5. D, F. & D, V.E., 1991. Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex, 1(1), pp.1-47.
  6. Daugman, J.G., 1985. Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. Journal of Optical Society of America, 2(7), pp.1160-1169.
  7. Engel, S., Zhang, X. & Wandell, B., 1997. Colour tuning in human visual cortex measured with functional magnetic resonance imaging. Nature, 388(6637), pp.68-71.
  8. Fukushima, K., Miyake, S. & Ito, T., Neocognitron: a neural network model for a mechanism of visual pattern recognition. In IEEE Transactions on Systems, Man, and Cybernetics. p. 826-834.
  9. Galbally, J., Marcel, S. & Fierrez, J., 2014. Image quality assessment for fake biometric detection: Application to Iris, fingerprint, and face recognition. IEEE Transactions on Image Processing, 23(2), pp.710-724.
  10. Grigorescu, S.E., Petkov, N. & Kruizinga, P., 2002. Comparison of texture features based on Gabor filters. IEEE transactions on image processing?: a publication of the IEEE Signal Processing Society, 11(10), pp.1160-1167.
  11. Hegdé, J. & Van Essen, D.C., 2000. Selectivity for complex shapes in primate visual area V2. The Journal of neuroscience?: the official journal of the Society for Neuroscience, 20(5), p.RC61.
  12. Hermosilla, G. et al., 2012. A comparative study of thermal face recognition methods in unconstrained environments. Pattern Recognition, 45(7), pp.2445- 2459.
  13. Hubel, D.H. & Wiesel, T.N., 1967. Receptive fields and functional architecture of monkey striate cortex. Journal of Physiology, 195(1), p.215-243.
  14. Van Kleef, J.P., Cloherty, S.L. & Ibbotson, M.R., 2010. Complex cell receptive fields: evidence for a hierarchical mechanism. Journal of Physiology, 588(18), pp.3457-3470.
  15. Kose, N., Apvrille, L. & Dugelay, J.-L., 2015. Facial makeup detection technique based on texture and shape analysis. In 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG). Ljubljana: IEEE, pp. 1-7.
  16. Lampl, L. et al., 2004. Intracellular Measurements of Spatial Integration and the MAX operation in complex cells of the cat primary visual cortex. Journal of Neurophysiology, 92, pp.2704-2713.
  17. LeCun, Y. et al., 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86, pp.2278-2324.
  18. Lei, Z. et al., 2007. Face recognition with local gabor textons. Advances in Biometrics, pp.49-57.
  19. Li, J. et al., 2004. Live face detection based on the analysis of fourier spectra. In Defense and Security. pp. 296- 303.
  20. Li, M. et al., 2013. Face recognition using early biologically inspired features. In Biometrics: Theory, Applications and Systems (BTAS), 2013 IEEE Sixth International Conference on. pp. 1-6.
  21. Lyons, M. et al., 1998. Coding facial expressions with Gabor wavelets. Proceedings - 3rd IEEE International Conference on Automatic Face and Gesture Recognition, FG 1998, pp.200-205.
  22. Maatta, J., Hadid, A. & Pietikäinen, M., 2011. Face spoofing detection from single images using microtexture analysis. In 2011 International Joint Conference on Biometrics (IJCB). pp. 1-7.
  23. Marcelja, S., 1980. Mathematical description of the responses of simple cortical cells. Journal of the Optical Society of America, 70, pp.1297-1300.
  24. McAdams, C.J. & Reid, R.C., 2005. Attention modulates the responses of simple cells in monkey primary visual cortex. The Journal of neuroscience?: the official journal of the Society for Neuroscience, 25, pp.11023- 11033.
  25. Meyers, E. & Wolf, L., 2008. Using biologically inspired features for face processing. International Journal of Computer Vision, 76(1), pp.93-104.
  26. Pan, G., Wu, Z. & Sun, L., 2008. Liveness detection for face recognition. Recent Advances in Face Recognition, (December), p.236.
  27. Perlibakas, V., 2006. Face Recognition using Principal Component Analysis and Log-Gabor Filters. Analysis, 3(February 2008), p.23.
  28. Petkov, N. & Kruizinga, P., 1997. Computational models of visual neurons specialised in the detection of periodic and aperiodic oriented visual stimuli: bar and grating cells. Biological cybernetics, 76, pp.83-96.
  29. Pisharady, P.K. & Martin, S., 2012. Pose invariant face recognition using neuro-biologically inspired features. International Journal of Future Computer Communications, 1(3), pp.316-320.
  30. Prokoski, F.J. & Riedel, R.B., 2002. Infrared identification of faces and body parts. Biometrics, pp.191-212.
  31. Raghavendra, R., Raja, K.B. & Busch, C., 2015. Presentation Attack Detection for Face Recognition Using Light Field Camera. Image Processing, IEEE Transactions on, 24(3), pp.1060-1075.
  32. Ramon, M., Caharel, S. & Rossion, B., 2011. The speed of recognition of personally familiar faces. Perception, 40(4), pp.437-49.
  33. Riesenhuber, M. & Poggio, T., 1999. Hierarchical models of object recognition in cortex. Nat. Neurosci., (2(11):1019-25).
  34. Riesenhuber, M. & Poggio, T., 2000. Models of object recognition. Nature Neuroscience, 3, pp.1199-1204.
  35. Rolls, E.T., 2012. Invariant Visual Object and Face Recognition: Neural and Computational Bases, and a Model, VisNet. Front Comp Neurosci, 6, p.35.
  36. Rose, N., 2006. Facial Expression Classification using Gabor and Log-Gabor Filters. In 7th International Conference on Automatic Face and Gesture Recognition, 2006. FGR 2006. pp. 346-350.
  37. Rust, N.C. et al., 2005. Spatiotemporal elements of macaque V1 receptive fields. Neuron, 46, pp.945-956.
  38. Van De Sande, K., Gevers, T. & Snoek, C., 2010. Evaluating color descriptors for object and scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(9), pp.1582-1596.
  39. SC37 ISO/IEC JTC1 & Biometrics, 2014. Information Technology-Presentation Attack Detection-Part 3: Testing, Reporting and Classification of Attacks, Schmid, A.M., Purpura, K.P. & Victor, J.D., 2014. Responses to orientation discontinuities in V1 and V2: physiological dissociations and functional implications. The Journal of neuroscience?: the official journal of the Society for Neuroscience, 34(10), pp.3559-78.
  40. Seal, A. et al., 2013. Automated thermal face recognition based on minutiae extraction. International Journal of Computational Intelligence Studies, 2(2), pp.133-156.
  41. Serrano, Á. et al., 2011. Analysis of variance of Gabor filter banks parameters for optimal face recognition. Pattern Recognition Letters, 32, pp.1998-2008.
  42. Serre, T. et al., 2007. Robust Object Recognition with Cortex-like mechanisms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(3), pp.411-426.
  43. Serre, T. & Riesenhuber, M., 2004. Realistic Modeling of Simple and Complex Cell Tuning in the HMAX Model , and Implications for Invariant Object Recognition in Cortex. Methods, p.017.
  44. Slavkovic, M. et al., 2013. Face recognition using Gabor filters, PCA and neural networks. In 2013 20th International Conference on Systems, Signals and Image Processing (IWSSIP). pp. 35-38.
  45. Wang, S. et al., 2013. Aging face identification using biologically inspired features. In 2013 IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC 2013). pp. 1-5.
  46. Wang, Y. & Chua, C., 2005. Face recognition from 2D and 3D images using 3D Gabor filters. Image and Vision Computing, 23(11), pp.1018-1028.
  47. Webster, M.A. & De Valois, R.L., 1985. Relationship between spatial-frequency and orientation tuning of striate-cortex cells. Journal of the Optical Society of America. A, Optics and image science, 2, pp.1124- 1132.
  48. Wen, D., Han, H. & Jain, A.K., 2015. Face spoof detection with distortion analysis. IEEE Transaction on Information Forensics and Security, 10(4), pp.746- 761.
  49. Wu, H.-Y. et al., 2012. Eulerian video magnification for revealing subtle changes in the world. ACM Transactions on Graphics, 31(4), pp.1-8.
  50. Yan, J. et al., 2012. Face liveness detection by exploring multiple scenic clues. In 12th International Conference on Control Automation Robotics & Vision (ICARCV). pp. 188-193.
  51. Yokono, J.J. & Poggio, T., 2004. Rotation Invariant Object Recognition from One Training Example.
  52. Zhang, W. et al., 2005. Local Gabor Binary Pattern Histogram Sequence (LGBPHS): A novel nonstatistical model for face representation and recognition. Proceedings of the IEEE International Conference on Computer Vision, I, pp.786-791.
  53. Zhang, Z. et al., 2012. A face antispoofing database with diverse attacks. Proceedings - 2012 5th IAPR International Conference on Biometrics, ICB 2012, pp.26-31.
Download


Paper Citation


in Harvard Style

Tsitiridis A., Conde C., Martín De Diego I. and Cabello E. (2017). Face Presentation Attack Detection using Biologically-inspired Features . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-225-7, pages 360-370. DOI: 10.5220/0006124603600370


in Bibtex Style

@conference{visapp17,
author={Aristeidis Tsitiridis and Cristina Conde and Isaac Martín De Diego and Enrique Cabello},
title={Face Presentation Attack Detection using Biologically-inspired Features},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={360-370},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006124603600370},
isbn={978-989-758-225-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)
TI - Face Presentation Attack Detection using Biologically-inspired Features
SN - 978-989-758-225-7
AU - Tsitiridis A.
AU - Conde C.
AU - Martín De Diego I.
AU - Cabello E.
PY - 2017
SP - 360
EP - 370
DO - 10.5220/0006124603600370