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A lite convolutional neural network built on permuted Xceptio-inception and Xceptio-reduction modules for texture based facial liveness recognition

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Abstract

Face recognition is one of the emerging areas in the field of biometric and computer vision that plays an important role in numerous time-bound applications such as ATM payment, criminal identification, E-Learning, healthcare, and online gaming. It can be compromised by various imposter attacks such as masks, print, or replay attacks. So, there is a requirement of a light-weight powerful classifier that could take significantly less time to minimize those effects by observing the liveness of a current person. In this paper, a lightweight permuted Xceptio-Inception/Reduction Convolutional Neural Network classifier has been proposed using depthwise convolution, permutation, reshape, and residual techniques for texture-based facial liveness recognition. It has been validated with moderately dense ImageNet benchmarked Convolutional Neural Network classifiers with respect to weight size, accuracy, precision, and recall. Here, we have considered some of the variants of most popular convolution neural networks such as AlexNet, Inception, ResNet, and VGGNet and applied these models for textured based facial liveness recognition. Before the training and testing of those classifiers, all the frontal face images from the FRAUD2, NUAA, and CASIA FASD imposter datasets had normalized, and the multi-colored space LBP feature maps extracted from these normalized image frames had supplied as inputs to the classifiers. The results show that the proposed convolutional neural network performs best among the above-standardized network models, whose total weights consumes less memory space, which leads to fast liveness face recognition. In the end, comparison with the previous work shows that it achieves almost the highest success rate and lowest Equal Error Rate as a non-intrusive classifier.

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References

  1. Alotaibi A, Mahmood A (2017) Deep face liveness detection based on nonlinear diffusion using convolution neural network. SIViP 11(4):713–720

    Article  Google Scholar 

  2. Angadi SA, Kagawade VC (2018) Detection of face spoofing using multiple texture descriptors. In: 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS). IEEE, pp 151–156

  3. Arashloo SR, Kittler J, Christmas W (2017) An anomaly detection approach to face spoofing detection: a new formulation and evaluation protocol. IEEE Access 5:13868–13882

    Article  Google Scholar 

  4. Banerji S, Verma A, Liu C (2012) LBP and color descriptors for image classification. In: Cross disciplinary biometric systems. Springer, Berlin, pp 205–225

  5. Beham MP, Roomi SMM (2018) Anti-spoofing enabled face recognition based on aggregated local weighted gradient orientation. SIViP 12(3):531–538

    Article  Google Scholar 

  6. Beham MP et al (2017) Face spoofing detection using binary gradient orientation pattern with deep neural network. In: 2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR). IEEE, pp 1–6

  7. Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828

    Article  Google Scholar 

  8. Benlamoudi A, Aiadi KE, Ouafi A, Samai D, Oussalah M (2017) Face antispoofing based on frame difference and multilevel representation. J Electron Imaging 26(4):043007

    Article  Google Scholar 

  9. Bhandare A et al (2016) Applications of convolutional neural networks. Int J Comput Sci Inf Technol 7(5):2206–2215

    Google Scholar 

  10. Bianco S, Cadene R, Celona L, Napoletano P (2018) Benchmark analysis of representative deep neural network architectures. IEEE Access 6:64270–64277

    Article  Google Scholar 

  11. Bjorck N et al (2018) Understanding batch normalization. In: Advances in neural information processing systems, pp 7694–7705

  12. Boulkenafet Z, Komulainen J, Hadid A (2015) Face anti-spoofing based on color texture analysis. In: 2015 IEEE international conference on image processing (ICIP). IEEE, pp 2636–2640

  13. Chakraborty S, Das D (2014) An overview of face liveness detection. arXiv preprint arXiv:1405.2227

  14. Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251–1258

  15. Das D, Chakraborty S (2014) Face liveness detection based on frequency and micro-texture analysis. In: 2014 International Conference on Advances in Engineering & Technology Research (ICAETR-2014). IEEE, pp 1–4

  16. Dong X, Shen J (2018) Triplet loss in siamese network for object tracking. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 459–474

  17. Dong J, Tian C, Xu Y (2017) Face liveness detection using color gradient features. In: 2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC). IEEE, pp 377–382

  18. Dong X, Shen J, Wu D, Guo K, Jin X, Porikli F (2019) Quadruplet network with one-shot learning for fast visual object tracking. IEEE Trans Image Process 28(7):3516–3527

    Article  MathSciNet  Google Scholar 

  19. Du X, Cai Y, Wang S, Zhang L (2016) Overview of deep learning. In: 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC). IEEE, pp 159–164

  20. Gulcehre C et al (2016) Noisy activation functions. In: International conference on machine learning, pp 3059–3068

  21. Hao H, Pei M, Zhao M (2019) Face liveness detection based on client identity using Siamese network. In: Chinese Conference on Pattern Recognition and Computer Vision (PRCV). Springer, Cham, pp 172–180

    Google Scholar 

  22. Hassaballah M, Murakami K, Ido S (2011) Eye and nose fields detection from gray scale facial images. In: MVA, pp 406–409

  23. He K et al (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  24. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167

  25. Ito K, Okano T, Aoki T (2017) Recent advances in biometrie security: a case study of liveness detection in face recognition. In: 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, pp 220–227

  26. Kim W, Suh S, Han JJ (2015) Face liveness detection from a single image via diffusion speed model. IEEE Trans Image Process 24(8):2456–2465

    Article  MathSciNet  Google Scholar 

  27. Kollreider K, Fronthaler H, Bigun J (2009) Non-intrusive liveness detection by face images. Image Vis Comput 27(3):233–244

    Article  Google Scholar 

  28. Koshy R, Mahmood A (2019) Optimizing deep CNN architectures for face Liveness detection. Entropy 21(4):423

    Article  Google Scholar 

  29. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  30. Kusuma IB et al (2018) Image spoofing detection using local binary pattern and local binary pattern variance. Int J Inf Commun Technol (IjoICT) 4(2):11–18

    Google Scholar 

  31. Lai Q et al (2019) Video saliency prediction using spatiotemporal residual attentive networks. IEEE Trans Image Process 29:1113–1126

    Article  MathSciNet  Google Scholar 

  32. Larbi K et al (2018) DeepColorFASD: face anti spoofing solution using a multi channeled color spaces CNN. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, pp 4011–4016

  33. Lee CE et al (2018) Towards building a remote anti-spoofing face authentication system. In: TENCON 2018-2018 IEEE Region 10 Conference. IEEE, pp 0321–0326

  34. Li L, Correia PL, Hadid A (2017) Face recognition under spoofing attacks: countermeasures and research directions. IET Biometrics 7(1):3–14

    Article  Google Scholar 

  35. Li K et al (2020) Object detection with convolutional neural networks. In: Hassaballah M, Awad AI (eds) Deep learning in computer vision: principles and applications. Taylor & Francis, Boca Raton, pp 41–62

    Chapter  Google Scholar 

  36. Liang Z, Shen J (2019) Local semantic siamese networks for fast tracking. IEEE Trans Image Process 29:3351–3364

    Article  Google Scholar 

  37. Luan X et al (2017) Face liveness detection with recaptured feature extraction. In: 2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC). IEEE, pp 429–432

  38. Mhou K, van der Haar D, Leung WS (2017) Face spoof detection using light reflection in moderate to low lighting. In: 2017 2nd Asia-Pacific Conference on Intelligent Robot Systems (ACIRS). IEEE, pp 47–52

  39. Nwankpa C et al (2018) Activation functions: comparison of trends in practice and research for deep learning. arXiv preprint arXiv:1811.03378

  40. Pan S, Deravi F (2019) Spatio-temporal texture features for presentation attack detection in biometric systems. In: 2019 Eighth International Conference on Emerging Security Technologies (EST). IEEE, pp 1–6

  41. Parveen S et al (2015) Face anti-spoofing methods. Curr Sci 108:1491–1500

    Google Scholar 

  42. Parveen S, Ahmad S, Abbas N, Adnan W, Hanafi M, Naeem N (2016) Face liveness detection using dynamic local ternary pattern (DLTP). Computers 5(2):10

    Article  Google Scholar 

  43. Pérez-Cabo D et al (2019) Deep anomaly detection for generalized face anti-spoofing. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops

  44. Pujol FA et al (2020) Entropy-based face recognition and spoof detection for security applications. Sustainability 12(1):85

    Article  Google Scholar 

  45. Qin L et al (2017) Content-independent face presentation attack detection with directional local binary pattern. In: Chinese Conference on Biometric Recognition. Springer, Cham, pp 118–126

  46. Raghavendra RJ, Kunte RS (2020) Extended local ternary co-relation pattern: a novel feature descriptor for face anti-spoofing. J Inf Secur Appl 52:102482

    Google Scholar 

  47. Rehman YAU, Po LM, Liu M (2017) Deep learning for face anti-spoofing: an end-to-end approach. In: 2017 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA). IEEE, pp 195–200

  48. Rehman YAU, Po LM, Liu M (2020) SLNet: stereo face liveness detection via dynamic disparity-maps and convolutional neural network. Expert Syst Appl 142:113002

    Article  Google Scholar 

  49. Ruder S (2016) An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747

  50. Şengür A et al (2018) Deep feature extraction for face liveness detection. In: 2018 International Conference on Artificial Intelligence and Data Processing (IDAP). IEEE, pp 1–4

  51. Seo J, Chung IJ (2019) Face liveness detection using thermal face-CNN with external knowledge. Symmetry 11(3):360

    Article  Google Scholar 

  52. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556

  53. Singh AK, Joshi P, Nandi GC (2014) Face recognition with liveness detection using eye and mouth movement. In: 2014 International Conference on Signal Propagation and Computer Technology (ICSPCT 2014). IEEE, pp 592–597

  54. Smith DF, Wiliem A, Lovell BC (2015) Binary watermarks: a practical method to address face recognition replay attacks on consumer mobile devices. In: IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015). IEEE, pp 1–6

  55. Song L, Liu C (2018) Face liveness detection based on joint analysis of rgb and near-infrared image of faces. Electron Imaging 2018(10):373–371

    Article  Google Scholar 

  56. Song L, Ma H (2019) Face liveness detection based on texture and color textures. In: 2019 International Conference on Cloud Computing and Big Data Analytics (ICCCBDA). IEEE, pp 418–422

  57. Song X et al (2020) Face anti-spoofing detection using least square weight fusion of channel-based feature classifiers (no. 2701). EasyChair

  58. Sthevanie F, Ramadhani KN (2018) Spoofing detection on facial images recognition using LBP and GLCM combination. J Phys Conf Ser 971:012014

    Article  Google Scholar 

  59. Sun W, Song Y, Chen C, Huang J, Kot AC (2020) Face spoofing detection based on local ternary label supervision in fully convolutional networks. IEEE Trans Inf Forensics Secur 15:3181–3196

    Article  Google Scholar 

  60. Szegedy C et al (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9

  61. Szegedy C et al (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826

  62. Tan X et al (2010) Face liveness detection from a single image with sparse low rank bilinear discriminative model. In: European conference on computer vision. Springer, Berlin, pp 504–517

  63. Uzun E et al (2018) rtCaptcha: a real-time CAPTCHA based Liveness detection system. In: NDSS

  64. van der Haar DT (2018) Real-time face antispoofing using Shearlets. In: International Information Security Conference. Springer, Cham, pp 16–29

  65. Vanitha A, Vaidehi V, Vasuhi S (2018) Liveliness detection in real time videos using color based chromatic moment feature. In: 2018 International Conference on Recent Trends in Advance Computing (ICRTAC). IEEE, pp 162–167

  66. Wang W, Shen J, Shao L (2017) Video salient object detection via fully convolutional networks. IEEE Trans Image Process 27(1):38–49

    Article  MathSciNet  Google Scholar 

  67. Wang SY, Yang SH, Chen YP, Huang JW (2017) Face liveness detection based on skin blood flow analysis. Symmetry 9(12):305

    Article  Google Scholar 

  68. Wang W, Shen J, Ling H (2018) A deep network solution for attention and aesthetics aware photo cropping. IEEE Trans Pattern Anal Mach Intell

  69. Wu B, Pan M, Zhang Y (2019) A review of face anti-spoofing and its applications in China. In: International conference on harmony search algorithm. Springer, Cham, pp 35–43

  70. Xie S et al (2017) Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1492–1500

  71. Yang T et al (2020) Evaluating facial recognition web services with adversarial and synthetic samples. Neurocomputing

  72. Yao C, Jia Y, di H, Wu Y (2020) Face spoofing detection using relativity representation on Riemannian manifold. IEEE Trans Inf Forensics Secur 15:3683–3693

    Article  Google Scholar 

  73. Ye M, Shen J, Lin G, Xiang T, Shao L, Hoi SC (2020) Deep learning for person re-identification: a survey and outlook. arXiv preprint arXiv:2001.04193

  74. Yılmaz AG, Turhal U, Nabiyev VV (2020) Effect of feature selection with meta-heuristic optimization methods on face spoofing detection. J Mod Technol Eng 5(1):48–59

    Google Scholar 

  75. Zhang W, Xiang S (2020) Face anti-spoofing detection based on DWT-LBP-DCT features. Signal Process Image Commun 89:115990

    Article  Google Scholar 

  76. Zhang Z et al (2012) A face antispoofing database with diverse attacks. In: 2012 5th IAPR international conference on Biometrics (ICB). IEEE, pp 26–31

  77. Zhang X, Zou J, He K, Sun J (2015) Accelerating very deep convolutional networks for classification and detection. IEEE Trans Pattern Anal Mach Intell 38(10):1943–1955

    Article  Google Scholar 

  78. Zhang LB, Peng F, Qin L, Long M (2018) Face spoofing detection based on color texture Markov feature and support vector machine recursive feature elimination. J Vis Commun Image Represent 51:56–69

    Article  Google Scholar 

  79. Zhao X, Lin Y, Heikkilä J (2017) Dynamic texture recognition using volume local binary count patterns with an application to 2D face spoofing detection. IEEE Trans Multimedia 20(3):552–566

    Article  Google Scholar 

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Acknowledgements

In our research, we have used the two most popular publicly available liveness face datasets, NUAA Imposter from Nanjing University of Aeronautics and Astronautics, China, and Face Replay Attack UQ Dataset-Version 2 from The University of Queensland, Australia. We are also very thankful to the Center for Biometrics and Security Research for providing us the CASIA Face Anti-spoofing database to support our research.

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  1. School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, 600127, India

    Ashutosh Satapathy & L. M. Jenila Livingston

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  1. Ashutosh Satapathy
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Correspondence to L. M. Jenila Livingston.

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Satapathy, A., Livingston, L.M.J. A lite convolutional neural network built on permuted Xceptio-inception and Xceptio-reduction modules for texture based facial liveness recognition. Multimed Tools Appl 80, 10441–10472 (2021). https://doi.org/10.1007/s11042-020-10181-4

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