Abstract
Face recognition and privacy protection are closely related. A high-quality facial image is required to achieve a high accuracy in face recognition; however, this undermines the privacy of the person being photographed. From the perspective of confidentiality, storing facial images as raw data is a problem. If a low-quality facial image is used, to protect user privacy, the accuracy of recognition decreases. In this paper, we propose a method for face recognition that solves these problems. We train a neural network with an unblurred image at first, and then train the neural network with a blurred image, using the features of the neural network trained with the unblurred image, as an initial value. This makes it possible to train features that are similar to the features trained with the neural network using a high-quality image. This enables us to perform face recognition without compromising user privacy. Our method consists of a neural network for face feature extraction, which extracts suitable features for face recognition from a blurred facial image, and a face recognition neural network. After pretraining both networks, we fine-tune them in an end-to-end manner. In experiments, the proposed method achieved accuracy comparable to that of conventional face recognition methods, which take as input unblurred face images from simulations and from images captured by our camera system.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2623–2631 (2019)
Asif, M.S., Ayremlou, A., Sankaranarayanan, A., Veeraraghavan, A., Baraniuk, R.G.: Flatcam: thin, lensless cameras using coded aperture and computation. IEEE Trans. Comput. Imag. 3(3), 384–397 (2016)
Best-Rowden, L., Bisht, S., Klontz, J.C., Jain, A.K.: Unconstrained face recognition: Establishing baseline human performance via crowdsourcing. In: IEEE International Joint Conference on Biometrics, pp. 1–8. IEEE (2014)
Browarek, S.: High resolution, Low cost, Privacy preserving Human motion tracking System via passive thermal sensing. Ph.D. thesis, Massachusetts Institute of Technology (2010)
Canh, T.N., Nagahara, H.: Deep compressive sensing for visual privacy protection in flatcam imaging. In: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 3978–3986. IEEE (2019)
Cannon, T., Fenimore, E.: Tomographical imaging using uniformly redundant arrays. Appl. Opt. 18(7), 1052–1057 (1979)
Chen, B.C., Chen, C.S., Hsu, W.H.: Face recognition and retrieval using cross-age reference coding with cross-age celebrity dataset. IEEE Trans. Multimedia 17(6), 804–815 (2015)
Chen, R., Mihaylova, L., Zhu, H., Bouaynaya, N.C.: A deep learning framework for joint image restoration and recognition. In: Circuits, Systems, and Signal Processing, pp. 1–20 (2019)
Chrysos, G.G., Zafeiriou, S.: Deep face deblurring. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 69–78 (2017)
Cossalter, M., Tagliasacchi, M., Valenzise, G.: Privacy-enabled object tracking in video sequences using compressive sensing. In: 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 436–441. IEEE (2009)
Dai, J., Wu, J., Saghafi, B., Konrad, J., Ishwar, P.: Towards privacy-preserving activity recognition using extremely low temporal and spatial resolution cameras. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 68–76 (2015)
Deng, J., Guo, J., Xue, N., Zafeiriou, S.: Arcface: additive angular margin loss for deep face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4690–4699 (2019)
Fernandes, F.E., Yang, G., Do, H.M., Sheng, W.: Detection of privacy-sensitive situations for social robots in smart homes. In: 2016 IEEE International Conference on Automation Science and Engineering (CASE), pp. 727–732. IEEE (2016)
Gade, R., Moeslund, T.B.: Thermal cameras and applications: a survey. Mach. Vis. Appl. 25(1), 245–262 (2014)
Gallego, G., et al.: Event-based vision: A survey. arXiv preprint arXiv:1904.08405 (2019)
Gupta, K., Bhowmick, B., Majumdar, A.: Motion blur removal via coupled autoencoder. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 480–484. IEEE (2017)
Hiura, S., Matsuyama, T.: Depth measurement by the multi-focus camera. In: Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No. 98CB36231), pp. 953–959. IEEE (1998)
Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: A database forstudying face recognition in unconstrained environments (2008)
Inagaki, Y., Kobayashi, Y., Takahashi, K., Fujii, T., Nagahara, H.: Learning to capture light fields through a coded aperture camera. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 418–434 (2018)
Jiao, S., Feng, J., Gao, Y., Lei, T., Yuan, X.: Visual cryptography in single-pixel imaging. arXiv preprint arXiv:1911.05033 (2019)
Jin, M., Hirsch, M., Favaro, P.: Learning face deblurring fast and wide. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 745–753 (2018)
Khan, S.S., Adarsh, V., Boominathan, V., Tan, J., Veeraraghavan, A., Mitra, K.: Towards photorealistic reconstruction of highly multiplexed lensless images. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 7860–7869 (2019)
Levin, A., Fergus, R., Durand, F., Freeman, W.T.: Image and depth from a conventional camera with a coded aperture. ACM Trans. Graph. (TOG) 26(3), 70 (2007)
Liang, C.K., Lin, T.H., Wong, B.Y., Liu, C., Chen, H.H.: Programmable aperture photography: multiplexed light field acquisition. ACM Trans. Graph. (TOG) 27, 55 (2008)
Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., Song, L.: Sphereface: Deep hypersphere embedding for face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 212–220 (2017)
Nagahara, H., Zhou, C., Watanabe, T., Ishiguro, H., Nayar, S.K.: Programmable aperture camera Using LCoS. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6316, pp. 337–350. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15567-3_25
Nguyen Canh, T., Nagahara, H.: Deep compressive sensing for visual privacy protection in flatcam imaging. In: Proceedings of the IEEE International Conference on Computer Vision Workshops (2019)
Nikonorov, A.V., Petrov, M., Bibikov, S.A., Kutikova, V.V., Morozov, A., Kazanskii, N.L.: Image restoration in diffractive optical systems using deep learning and deconvolution. Comput. Opt. 41(6), 875–887 (2017)
Nodari, A., Vanetti, M., Gallo, I.: Digital privacy: replacing pedestrians from google street view images. In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), pp. 2889–2893. IEEE (2012)
Padilla-López, J.R., Chaaraoui, A.A., Flórez-Revuelta, F.: Visual privacy protection methods: a survey. Exp. Syst. Appl. 42(9), 4177–4195 (2015)
Pittaluga, F., Koppal, S.J.: Privacy preserving optics for miniature vision sensors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 314–324 (2015)
Pittaluga, F., Koppal, S.J.: Pre-capture privacy for small vision sensors. IEEE Trans. Pattern Anal. Mach. Intell. 39(11), 2215–2226 (2016)
Raskar, R.: Less is more: coded computational photography. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007. LNCS, vol. 4843, pp. 1–12. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-76386-4_1
Ren, D., Zhang, K., Wang, Q., Hu, Q., Zuo, W.: Neural blind deconvolution using deep priors. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)
Ren, D., Zuo, W., Zhang, D., Xu, J., Zhang, L.: Partial deconvolution with inaccurate blur kernel. IEEE Trans. Image Process. 27(1), 511–524 (2017)
Ren, W., et al.: Deep non-blind deconvolution via generalized low-rank approximation. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31, pp. 297–307. Curran Associates, Inc. (2018). http://papers.nips.cc/paper/7313-deep-non-blind-deconvolution-via-generalized-low-rank-approximation.pdf
Schuler, C.J., Christopher Burger, H., Harmeling, S., Scholkopf, B.: A machine learning approach for non-blind image deconvolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1067–1074 (2013)
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017)
Shen, Z., Lai, W.S., Xu, T., Kautz, J., Yang, M.H.: Deep semantic face deblurring. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8260–8269 (2018)
Sinha, A., Lee, J., Li, S., Barbastathis, G.: Lensless computational imaging through deep learning. Optica 4(9), 1117–1125 (2017)
Sloane, N.J., Harwitt, M.: Hadamard transform optics (1979)
Son, H., Lee, S.: Fast non-blind deconvolution via regularized residual networks with long/short skip-connections. In: 2017 IEEE International Conference on Computational Photography (ICCP), pp. 1–10. IEEE (2017)
Tai, Y., Yang, J., Liu, X., Xu, C.: Memnet: a persistent memory network for image restoration. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4539–4547 (2017)
Thorpe, C., Li, F., Li, Z., Yu, Z., Saunders, D., Yu, J.: A coprime blur scheme for data security in video surveillance. IEEE Trans. Pattern Anal. Mach. Intell. 35(12), 3066–3072 (2013)
Veeraraghavan, A., Raskar, R., Agrawal, A., Mohan, A., Tumblin, J.: Dappled photography: mask enhanced cameras for heterodyned light fields and coded aperture refocusing. ACM Trans. Graph. (TOG). 26, 69 (2007)
Wang, H., et al.: Cosface: large margin cosine loss for deep face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5265–5274 (2018)
Wang, R., Tao, D.: Training very deep CNNs for general non-blind deconvolution. IEEE Trans. Image Process. 27(6), 2897–2910 (2018)
Wang, Z.W., Vineet, V., Pittaluga, F., Sinha, S.N., Cossairt, O., Bing Kang, S.: Privacy-preserving action recognition using coded aperture videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2019)
Xu, L., Ren, J.S., Liu, C., Jia, J.: Deep convolutional neural network for image deconvolution. In: Advances in Neural Information Processing Systems, pp. 1790–1798 (2014)
Zhang, K., Xue, W., Zhang, L.: Non-blind image deconvolution using deep dual-pathway rectifier neural network. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2602–2606. IEEE (2017)
Zhang, L., Zuo, W.: Image restoration: from sparse and low-rank priors to deep priors [lecture notes]. IEEE Signal Process. Mag. 34(5), 172–179 (2017)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Ishii, Y., Sato, S., Yamashita, T. (2020). Privacy-Aware Face Recognition with Lensless Multi-pinhole Camera. In: Bartoli, A., Fusiello, A. (eds) Computer Vision – ECCV 2020 Workshops. ECCV 2020. Lecture Notes in Computer Science(), vol 12539. Springer, Cham. https://doi.org/10.1007/978-3-030-68238-5_35
Download citation
DOI: https://doi.org/10.1007/978-3-030-68238-5_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-68237-8
Online ISBN: 978-3-030-68238-5
eBook Packages: Computer ScienceComputer Science (R0)