Optimal feature-level fusion and layered k-support vector machine for spoofing face detection

Article

Abstract

The recognition frameworks are highly vulnerable to spoofing attacks and this vulnerability generates an effective security concerned issues in biometric domain. Moreover, some of the earlier proposed approaches have attained attractive results with intra test (i.e. by training and testing the system on same database) evaluation done to detect the face spoofing attack. Consequently, most of these techniques generate incorrect decision on the recognition of genuine faces with unseen attacks in case of inter test evaluation (i.e. the system is trained on one database and then tested on another database). However, this impact is considered as a major difficulty in the highly focused biometric anti-spoofing research domain. In this work, we propose a multimodal biometric framework for the accurate recognition of fake face from genuine face. Initially, face image features which are coupled to the color spaces HSV and YCbCr are extracted with EDGHM-SURF (Enhanced Discrete Gaussian-Hermite Moment based Speed-up Robust Feature) descriptor. Then, a newly developed method of feature-level fusion using OGWO (FLFO) is used to fuse these extracted features. This method utilized the OGWO (Oppositional Gray Wolf Optimization) algorithm due to its excellent exploitation and exploration behavior in the identification of optimal weight score from the solution space, without allowing the solutions to stick in the local optimum. Finally, the fused features are fed into the Layered k-SVM (k-support vector machine) classifier for the recognition of fake face. The experimental results of our proposed approach are evaluated on three traditional benchmark face spoofing databases, namely the Replay-Attack, the CASIA Face Anti-Spoofing, and the MSU Mobile Face Spoof database. The outcome of our proposed approach exhibited steady and robust performance across all the three datasets. More commonly, our proposed approach executes well in the inter database tests and yields high performance, even though when only operated with minimized training data.

Keywords

Face recognition Oppositional grey wolf optimizer Spoofing detection Feature level fusion Layered k-SVM EDGHM-SURF 

References

  1. 1.
    Alcantarilla PF, Bergasa LM, Davison AJ (2013) Gauge-SURF descriptors. Image Vis Comput 31(1):103–116CrossRefGoogle Scholar
  2. 2.
    Anjos A, Marcel S (2011) Counter-measures to photo attacks in face recognition: a public database and a baseline. In Proceedings of IAPR IEEE International Joint Conference on Biometrics (IJCB)Google Scholar
  3. 3.
    Bay H, Tuytelaars T, Gool L (2006) Computer Vision – ECCV 2006: 9th European Conference on Computer Vision, Graz, Austria. Proceedings, Part I. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006, ch. SURF: Speeded Up Robust Features, pp.404–417. [Online]. Available  https://doi.org/10.1007/11744023 3
  4. 4.
    Bay H, Tuytelaars T, Gool LV (2008) Speeded Up Robust Features (SURF). Comput Vis Image Underst 110:346–359CrossRefGoogle Scholar
  5. 5.
    Bharadwaj S, Dhamecha TI, Vatsa M, Richa S (2013) Computationally efficient face spoofing detection with motion magnification. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Workshop on BiometricsGoogle Scholar
  6. 6.
    Bishop CM (2006) Pattern recognition. Mach Learn 128Google Scholar
  7. 7.
    Boulkenafet Z, Komulainen J, Hadid A (2015) Face anti-spoofing based on color texture analysis. In IEEE International Conference on Image Processing (ICIP2015)Google Scholar
  8. 8.
    Chiang C-L (2005) Improved genetic algorithm for power economic dispatch of units with valve-point effects and multiple fuels. IEEE Trans Power Syst 20(4):1690–1699MathSciNetCrossRefGoogle Scholar
  9. 9.
    Chingovska I, Anjos A, Marcel S (2012) On the effectiveness of local binary patterns in face anti-spoofing. In International Conference of the Biometrics Special Interest Group (BIOSIG), pp 1–7Google Scholar
  10. 10.
    Cui F, Yang G (2011) Score level fusion of fingerprint and finger vein recognition. J Comput Inf Sys 7(16):5723–5731Google Scholar
  11. 11.
    Cui J, Liu Y, Xu Y, Zhao H, Zha H (2013) Tracking generic human motion via fusion of low-and high-dimensional approaches. IEEE Trans Syst Man Cybern Syst 43(4):996–1002CrossRefGoogle Scholar
  12. 12.
    Dass SC, Nandakumar K, Jain AK (2005) A principled approach to score level fusion in multimodal biometric systems. In AVBPA, pp 1049–1058Google Scholar
  13. 13.
    de Freitas Pereira T, Komulainen J, Anjos A, De Martino JM, Hadid A, Pietikäinen M, Marcel S (2014) Face liveness detection using dynamic texture. EURASIP J Image Video Processing, 2014(1):2Google Scholar
  14. 14.
    de Freitas Pereira T, Anjos A, De Martino J, Marcel S (2013) Can face anti-spoofing countermeasures work in a real world scenario? In International Conference on Biometrics (ICB), pp 1–8Google Scholar
  15. 15.
    Deepak A, Shirsat S (2016) Multimodal biometric recognition system. Proc. of Int. Conf. on recent Innovations in Engineering and Management, pp 237–244Google Scholar
  16. 16.
    Durgesh KS, Lekha B (2010) Data classification using support vector machine. J Theor Appl Inf Technol 12(1):1–7Google Scholar
  17. 17.
    Feng L, Po L-M, Li Y, Xu X, Yuan F, Cheung TC-H, Cheung K-W (2016) Integration of image quality and motion cues for face anti-spoofing: A neural network approach. J Vis Commun Image Represent 38:451–460CrossRefGoogle Scholar
  18. 18.
    Galbally J, Marcel S (2014) Face anti-spoofing based on general image quality assessment. In Proc. IAPR/IEEE Int. Conf. on Pattern Recognition, ICPR, pp 1173–1178Google Scholar
  19. 19.
    Galbally J, Marcel S, Fierrez J (2014) Biometric antispoofing methods: a survey in face recognition. IEEE Access 2:1530–1552CrossRefGoogle Scholar
  20. 20.
    Gao W, Liu S, Huang L (2012) A global best artificial bee colony algorithm for global optimization. J Comput Appl Math 236:2741–2753MathSciNetCrossRefMATHGoogle Scholar
  21. 21.
    Gragnaniello D, Poggi G, Sansone C, Verdoliva L (2015) An investigation of local descriptors for biometric spoofing detection. IEEE trans Inf Forensics Secur 10(4):849–863CrossRefGoogle Scholar
  22. 22.
    Jain AK, Ross A, Prabhakar S (2004) An introduction to biometric recognition. IEEE Trans Circuits Syst Video Technol 14(1):4–20CrossRefGoogle Scholar
  23. 23.
    Juan L, Gwun O (2012) A comparison of SIFT, PCA-SIFT and SURF. Int J Image Process 3(4):143–152Google Scholar
  24. 24.
    Karaboga D, Ozturk C (2011) A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Appl Soft Comput 11:652–657CrossRefGoogle Scholar
  25. 25.
    Kollreider K, Fronthaler H, Faraj MI, Bigun J (2007) Real-time face detection and motion analysis with application in liveness assessment. IEEE Trans Inf Forensics Secur 2(3):548–558CrossRefGoogle Scholar
  26. 26.
    Lindeberg T (1998) Feature detection with automatic scale selection. Int J Comput Vis 30(2):77–116Google Scholar
  27. 27.
    Liu Y, Nie L, Han L, Zhang L, Rosenblum DS (2015) Action2Activity: Recognizing Complex Activities from Sensor Data. In IJCAI, pp 1617–1623Google Scholar
  28. 28.
    Liu L, Cheng L, Liu Y, Jia Y, Rosenblum DS (2016) Recognizing Complex Activities by a Probabilistic Interval-Based Model. In AAAI, vol. 30, pp 1266–1272Google Scholar
  29. 29.
    Liu Y, Zhang L, Nie L, Yan Y, Rosenblum DS (2016) Fortune Teller: Predicting Your Career Path. In AAAI, pp 201–207Google Scholar
  30. 30.
    Liu Y, Zheng Y, Liang Y, Liu S, Rosenblum DS (2016) Urban water quality prediction based on multi-task multi-view learning. IJCAI, pp 2576–2581.Google Scholar
  31. 31.
    Lowe DG (2004) Distinctive image features from scale-invariant key-points. Int J Comput Vis 60(2):91–110CrossRefGoogle Scholar
  32. 32.
    Lu Y, Wei Y, Liu L, Zhong J, Sun L, Liu Y (2017) Towards unsupervised physical activity recognition using smart phone accelerometers. Multimed Tools Appl 76(8):10701–10719CrossRefGoogle Scholar
  33. 33.
    Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61CrossRefGoogle Scholar
  34. 34.
    Ojala T, Pietikäinen M, Mäenpaa T (2002) Multiresolution grayscale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell (PAMI) 24(7):971–987CrossRefMATHGoogle Scholar
  35. 35.
    Park G, Kim S (2013) Hand biometric recognition based on fused hand geometry and vascular patterns. Sensors 13:2895–2910CrossRefGoogle Scholar
  36. 36.
    Park GT, Kim S (2013) Hand biometric recognition based on fused hand geometry and vascular patterns. Sensors 13(3):2895–2910CrossRefGoogle Scholar
  37. 37.
    Prabhakar S, Pankanti S, Jain AK (2003) Biometric recognition: security and privacy concerns. IEEE Secur Priv 1(2):33–42CrossRefGoogle Scholar
  38. 38.
    Pradhan M, Roy PK, Pal T (2017) Oppositional based grey wolf optimization algorithm for economic dispatch problem of power system. Ain Shams Eng J.  https://doi.org/10.1016/j.asej.2016.08.023
  39. 39.
    Rastislav Lukac KNP (2007) Color image processing: Methods and applications, vol 8. CRC, New YorkMATHGoogle Scholar
  40. 40.
    Rattani A, Kisku DR, Bicego M, Tistarelli M (2007) Feature level fusion of face and fingerprint biometrics. In Biometrics: Theory, Applications, and Systems, 2007. BTAS 2007. First IEEE International Conference on, pp 1–6. IEEEGoogle Scholar
  41. 41.
    Ross A, Govindarajan R (2004) Feature level fusion in biometric systems. In proceedings of Biometric Consortium Conference (BCC), pp 1–2Google Scholar
  42. 42.
    Sanchez J, Perronnin F (2011) High-dimensional signature compression for large-scale image classification. In Computer Vision and Pattern Recognition (CVPR), 2011 I.E. Conference on. IEEE, pp 1665–1672Google Scholar
  43. 43.
    Smith DF, Wiliem A, Lovell BC (2015) Face recognition on consumer devices: Reflections on replay attacks. IEEE Trans Inf Forensics Secur 10(4):236–245CrossRefGoogle Scholar
  44. 44.
    Srivastava DK, Bhambhu L (2009) Data classification using support vector machine. J Theor Appl Inf Technol 12(1):1–7Google Scholar
  45. 45.
    Sundararaj V (2016) An efficient threshold prediction scheme for wavelet based ECG signal noise reduction using variable step size firefly algorithm. Int J Intell Eng Syst 9(3):117–126CrossRefGoogle Scholar
  46. 46.
    Tirunagari S, Poh N, Windridge D, Iorliam A, Suki N, Ho ATS (2015) Detection of face spoofing using visual dynamics. IEEE Trans Inf Forensics Secur 10(4):762–777CrossRefGoogle Scholar
  47. 47.
    Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. In Computational intelligence for modelling, control and automation, 2005 and international conference on intelligent agents, web technologies and internet commerce, international conference on, vol. 1, pp 695–701. IEEEGoogle Scholar
  48. 48.
    Wang T, Yang J, Lei Z, Liao S, Li SZ (2013) Face liveness detection using 3D structure recovered from a single camera. In IAPR International Conference on Biometrics, ICBGoogle Scholar
  49. 49.
    Wen D, Han H, Jain A (2015) Face spoof detection with image distortion analysis. Trans Inf Forensics Secur 10(4):746–761CrossRefGoogle Scholar
  50. 50.
    Yang B, Dai M (2011) Image analysis by Gaussian-Hermite moments. Signal Process 91(10):2290–2303CrossRefMATHGoogle Scholar
  51. 51.
    Yang J, Lei Z, Li SZ (2014) Learn convolutional neural network for face anti-spoofing. CoRR abs/1408.5601Google Scholar
  52. 52.
    Yang W, Huang X, Zhou F, Liao Q (2014) Comparative competitive coding for personal identification by using finger vein and finger dorsal texture fusion. Inf Sci 268:20–32CrossRefGoogle Scholar
  53. 53.
    Zhang Z, Yan J, Liu S, Lei Z, Yi D, Li SZ (2012) A face antispoofing database with diverse attacks. In 5th IAPR International Conference on Biometrics (ICB), pp 26–31Google Scholar
  54. 54.
    Zheng L, Wang S, Tian Q (2014) Coupled binary embedding for large-scale image retrieval. IEEE Trans Image Process 23(8):3368–3380MathSciNetCrossRefMATHGoogle Scholar
  55. 55.
    Zheng L, Wang S, Tian L, He F, Liu Z, Tian Q (2015) Query-adaptive late fusion for image search and person re-identification. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1741–1750Google Scholar
  56. 56.
    Zheng L, Yang Y, Tian Q (2017) SIFT meets CNN: A decade survey of instance retrieval. IEEE Journal of Latex class files, 14(8), August 2015.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringR.M.K. Engineering CollegeKavaraipettaiIndia
  2. 2.Department of Information & TechnologyR.M.K Engineering CollegeKavaraipettaiIndia

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