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Proposing a sparse representational based face verification system to run in a shortage of memory

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Abstract

Studying face verification has seen tremendous growth over the past years. During the last decade, with the improvement of system processors and memories, deep learning was growth widely and the applications of Convolutional Neural Network (CNN) affected all image processing tasks. But, needing much space to save several parameters of learned model is still a big challenge to use them on simple devices, e.g. cell phones. In this paper, to address the problem of face verification in a shortage of memory sparse representation has been employed. So, to compare two portraits a dictionary is generated from each image using augmentation techniques. Then, each face is reconstructed sparsely by the other dictionary and if there is a negligible average of reconstruction error, couple of faces are matched. The proposed method has been assessed in various conditions of several face datasets and the results show improvement comparing to all sparse representational approaches. Although the evaluations indicate a bit less accuracy than CNN-based methods, the main advantage is less usage of memory that can lead running on mobile devices.

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References

  1. Allison PD (2000) Multiple imputation for missing data: a cautionary tale. Sociol Methods Res 28(3):301–309

    Article  Google Scholar 

  2. Becker S, Bobin J, Candés E J (2011) NESTA: a fast and accurate first-order method for sparse recovery. SIAM J Imaging Sci 4(1):1–39

    Article  MathSciNet  Google Scholar 

  3. Bengio Y, et al. (2009) Learning deep architectures for ai. Foundations and trends®;. Mach Learn 2(1):1–127

    Article  MathSciNet  Google Scholar 

  4. Cai D, He X, Hu Y, Han J, Huang T (2007) Learning a spatially smooth subspace for face recognition. In: Proc. IEEE Conf. computer vision and pattern recognition machine learning (CVPR’07)

  5. Chen BC, Chen CS, Hsu WH (2014) Cross-age reference coding for age-invariant face recognition and retrieval. In: European conference on computer vision. Springer, pp 768–783

  6. Chen B, Yang Z, Huang S, Du X, Cui Z, Bhimani J, Xie X, Mi N (2017) Cyber-physical system enabled nearby traffic flow modelling for autonomous vehicles, IEEE

  7. Choudhury B, Then P, Issac B, Raman V, Haldar MK (2018) A survey on biometrics and cancelable biometrics systems. Int J Image Graph 18(01):1850,006

    Article  Google Scholar 

  8. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE Computer society conference on computer vision and pattern recognition, 2005. CVPR 2005, vol 1. IEEE, pp 886–893

  9. Dhamecha TI, Nigam A, Singh R, Vatsa M (2013) Disguise detection and face recognition in visible and thermal spectrums. In: 2013 International conference on biometrics (ICB). IEEE, pp 1–8

  10. Donoho DL (2006) Compressed sensing. IEEE Trans Inform Theory 52 (4):1289–1306

    Article  MathSciNet  Google Scholar 

  11. Emadi M, Navabifar F, Khalid M, Yusof R (2011) A review of methods for face verification under illumination variation. In: Proceedings of the international conference on image processing, computer vision, and pattern recognition (IPCV). Citeseer, p 1

  12. Fard SMH, Hashemi S (2018) Sparse representation using deep learning to classify multi-class complex data. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 1–11

  13. Fawcett T (2006) An introduction to roc analysis. Pattern Recogn Lett 27 (8):861–874

    Article  MathSciNet  Google Scholar 

  14. Galbally J, Marcel S, Fierrez J, et al. (2014) Biometric antispoofing methods: a survey in face recognition. IEEE Access 2(1530-1552):1

    MATH  Google Scholar 

  15. Guo H, Wang R, Choi J, Davis LS (2012) Face verification using sparse representations. In: 2012 IEEE Computer society conference on computer vision and pattern recognition workshops (CVPRW). IEEE, pp 37–44

  16. Hassner T, Harel S, Paz E, Enbar R (2015) Effective face frontalization in unconstrained images. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4295–4304

  17. Hassner T, Masi I, Kim J, Choi J, Harel S, Natarajan P, Medioni G (2016) Pooling faces: template based face recognition with pooled face images. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 59–67

  18. He R, Zheng WS, Hu BG, Kong XW (2011) A regularized correntropy framework for robust pattern recognition. Neural Comput 23(8):2074–2100

    Article  Google Scholar 

  19. He L, Li H, Zhang Q, Sun Z (2018) Dynamic feature learning for partial face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7054–7063

  20. Hernández-García A, König P (2018) Do deep nets really need weight decay and dropout? arXiv:https://arxiv.org/abs/180207042

  21. Hu G, Yang Y, Yi D, Kittler J, Christmas W, Li SZ, Hospedales T (2015) When face recognition meets with deep learning: an evaluation of convolutional neural networks for face recognition. In: Proceedings of the IEEE international conference on computer vision workshops, pp 142–150

  22. Huang GB, Mattar M, Berg T, Learned-Miller E (2008) Labeled faces in the wild: a database forstudying face recognition in unconstrained environments. In: Workshop on faces in’Real-Life’Images: detection, alignment, and recognition

  23. Jin X, Liu Y, Li X, Zhao G, Chen Y, Guo K (2015) Privacy preserving face identification in the cloud through sparse representation. In: Chinese conference on biometric recognition. Springer, pp 160–167

  24. Jones M, Viola P (2003) Fast multi-view face detection. Mitsubishi Electric Research Lab TR-20003-96 3(14):2

    Google Scholar 

  25. Koestinger M, Hirzer M, Wohlhart P, Roth PM, Bischof H (2012) Large scale metric learning from equivalence constraints. In: 2012 IEEE Conference on computer vision and pattern recognition (CVPR). IEEE, pp 2288–2295

  26. Kohli N, Yadav D, Noore A (2018) Face verification with disguise variations via deep disguise recognizer. In: CVPR Workshop on disguised faces in the wild, vol 4

  27. Krishna Prasad K, Aithal P (2017) A conceptual study on user identification and verification process using face recognition techniques

  28. Leng L, Zhang J, Khan MK, Chen X, Alghathbar K (2010) Dynamic weighted discrimination power analysis: a novel approach for face and palmprint recognition in dct domain. Int J Phys Sci 5(17):2543–2554

    Google Scholar 

  29. Leng L, Zhang J, Xu J, Khan MK, Alghathbar K (2010) Dynamic weighted discrimination power analysis in dct domain for face and palmprint recognition. In: 2010 International conference on information and communication technology convergence (ICTC). IEEE, pp 467–471

  30. Leng L, Zhang J, Chen G, Khan MK, Alghathbar K (2011) Two-directional two-dimensional random projection and its variations for face and palmprint recognition. In: International conference on computational science and its applications. Springer, pp 458–470

  31. Leng L, Zhang S, Bi X, Khan MK (2012) Two-dimensional cancelable biometric scheme. In: 2012 International conference on wavelet analysis and pattern recognition. IEEE, pp 164–169

  32. Mairal J (2014) SPAMS: a SPArse modeling software v2.5

  33. Martinez andR AM (1998) Benavente, the AR face database. Tech. rep., CVC Technical Report

  34. Melgor (2018) Demystifying face recognition. https://melgor.github.io/blcv.github.io/static/2018/02/27/demystifying-face-recognition-v-data-augmentation/index.html

  35. Miri M (2017) Face verification in the wild using similarity in representations. In: Artificial intelligence and signal processing conference (AISP), 2017. IEEE, pp 140–144

  36. Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Article  Google Scholar 

  37. Ojansivu V, Heikkilä J (2008) Blur insensitive texture classification using local phase quantization. In: International conference on image and signal processing. Springer, pp 236–243

  38. Ortiz EG, Becker BC (2014) Face recognition for web-scale datasets. Comput Vis Image Underst 118:153–170

    Article  Google Scholar 

  39. Osadchy M, Pinkas B, Jarrous A, Moskovich B (2010) Scifi-a system for secure face identification. In: 2010 IEEE symposium on security and privacy. IEEE, pp 239–254

  40. Palm C, Keysers D, Lehmann T, Spitzer K (2000) Gabor filtering of complex hue/saturation images for color texture classification. In: Proc. JCIS. Citeseer, pp 45–49

  41. Parkhi OM, Vedaldi A, Zisserman A, et al. (2015) Deep face recognition. In: BMVC, vol 1, p 6

  42. Polikar R (2009) Ensemble learning. Scholarpedia 4(1):2776. https://doi.org/10.4249/scholarpedia.2776. revision #186077

    Article  Google Scholar 

  43. Ramanathan N, Chellappa R, Chowdhury AR (2004) Facial similarity across age, disguise, illumination and pose. In: 2004 International conference on image processing, 2004. ICIP’04, vol 3. IEEE, pp 1999–2002

  44. Ramirez C, Kreinovich V, Argaez M (2013) Why l1 is a good approximation to l0: a geometric explanation. J Uncert Syst 7(3):203–207

    Google Scholar 

  45. Rao RPN, Olshausen BA, Lewicki MS (2002) Probabilistic models of the brain: perception and neural function. MIT press

  46. Ratner AJ, Ehrenberg H, Hussain Z, Dunnmon J, Ré C (2017) Learning to compose domain-specific transformations for data augmentation. In: Advances in neural information processing systems, pp 3236–3246

  47. Sanguansat P (2010) Two-dimensional random projection for face recognition. In: 2010 First international conference on pervasive computing, signal processing and applications. IEEE, pp 1107–1110

  48. Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117

    Article  Google Scholar 

  49. Schroff F, Kalenichenko D, Philbin J (2015) Facenet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 815–823

  50. Simard PY, LeCun YA, Denker JS, Victorri B (1998) Transformation invariance in pattern recognition—tangent distance and tangent propagation. In: Neural networks: tricks of the trade. Springer, pp 239–274

  51. Sun Y, Liang D, Wang X, Tang X (2015) Deepid3: Face recognition with very deep neural networks. arXiv:https://arxiv.org/abs/150200873

  52. Taigman Y, Yang M, Ranzato M, Wolf L (2014) Deepface: closing the gap to human-level performance in face verification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1701–1708

  53. Tan X, Triggs B (2007) Enhanced local texture feature sets for face recognition under difficult lighting conditions. In: International workshop on analysis and modeling of faces and gestures. Springer, pp 168–182

  54. Taylor L, Nitschke G (2017) Improving deep learning using generic data augmentation. arXiv:https://arxiv.org/abs/170806020

  55. Turcot P, Lowe DG (2009) Better matching with fewer features: the selection of useful features in large database recognition problems. In: 2009 IEEE 12th International conference on computer vision workshops (ICCV Workshops). IEEE, pp 2109–2116

  56. Wright SJ (2015) Coordinate descent algorithms. Math Program 151(1):3–34

    Article  MathSciNet  Google Scholar 

  57. Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31 (2):210–227. https://doi.org/10.1109/TPAMI.2008.79

    Article  Google Scholar 

  58. Wright J, Ma Y, Mairal J, Sapiro G, Huang TS, Yan S (2010) Sparse representation for computer vision and pattern recognition. Proc IEEE 98(6):1031–1044

    Article  Google Scholar 

  59. Yan M, Liu H, Xu X, Song E, Qian Y, Pan N, Jin R, Jin L, Cheng S, Hung CC (2017) An improved label fusion approach with sparse patch-based representation for mri brain image segmentation. Int J Imaging Syst Technol 27(1):23–32

    Article  Google Scholar 

  60. Yang Z, Jia D, Ioannidis S, Mi N, Sheng B (2018) Intermediate data caching optimization for multi-stage and parallel big data frameworks. In: 2018 IEEE 11th international conference on cloud computing (CLOUD). IEEE, pp 277–284

  61. Yuan XT, Liu X, Yan S (2012) Visual classification with multitask joint sparse representation. IEEE Trans Image Process 21(10):4349–4360. https://doi.org/10.1109/TIP.2012.2205006

    Article  MathSciNet  MATH  Google Scholar 

  62. Zhang Z, Xu Y, Yang J, Li X, Zhang D (2015) A survey of sparse representation: algorithms and applications. IEEE access 3:490–530

    Article  Google Scholar 

  63. Zhao W, Chellappa R, Phillips PJ, Rosenfeld A (2003) Face recognition: a literature survey. ACM Comput Surv (CSUR) 35(4):399–458

    Article  Google Scholar 

  64. Zhao J, Xie X, Xu X, Sun S (2017) Multi-view learning overview: recent progress and new challenges. Inform Fusion 38:43–54

    Article  Google Scholar 

  65. Zhu X, Ramanan D (2012) Face detection, pose estimation, and landmark localization in the wild. In: 2012 IEEE Conference on computer vision and pattern recognition (CVPR). IEEE, pp 2879–2886

  66. Zhu Q, Sun H, Feng Q, Wang J (2014) Cceda: building bridge between subspace projection learning and sparse representation-based classification. Electron Lett 50 (25):1919–1921

    Article  Google Scholar 

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Acknowledgments

The authors would like to thank professors Ali Ghodsi, Mohammad Taheri and Ali Dehghan Tanha for their insightful instructions.

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  1. Department of Computer Science and Engineering, Shiraz University, Shiraz, Iran

    Seyed Mehdi Hazrati Fard & Sattar Hashemi

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  1. Seyed Mehdi Hazrati Fard
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Correspondence to Sattar Hashemi.

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Hazrati Fard, S.M., Hashemi, S. Proposing a sparse representational based face verification system to run in a shortage of memory. Multimed Tools Appl 79, 2965–2985 (2020). https://doi.org/10.1007/s11042-019-08491-3

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