Abstract
Facial recognition has been one of the most intriguing, interesting research topics over years. It involves some specific face-based algorithms such as facial detection, facial alignment, facial representation, and facial recognition as well; however, all of these algorithms are derived from heavy deep learning architectures, which leads to limitations on development, scalability, flawed accuracy, and deployment into publicity with mere CPU servers. It also requires large datasets containing hundreds of thousands of records for training purposes. In this paper, we propose a full pipeline for an effective face recognition application which only uses a small Vietnamese-celebrity datasets and CPU for training that can solve the leakage of data and the need for GPU devices. It is based on a face vector-to-string tokens algorithm then saves face’s properties into Elasticsearch for future retrieval, so the problem of online learning in Facial Recognition is also tackled. In comparison with another popular algorithms on the dataset, our proposed pipeline achieves not only higher accuracy, but also faster inference time for real-time face recognition applications.
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References
Almabdy, S., Elrefaei, L.: Deep convolutional neural network-based approaches for face recognition. Appl. Sci. 9, 4397 (2019). https://doi.org/10.3390/app9204397
Amos, B., Ludwiczuk, B., Satyanarayanan, M.: OpenFace: a general-purpose face recognition library with mobile applications. Technical report CMU-CS-16-118, CMU School of Computer Science (2016)
Bradski, G.: The OpenCV library. Dr. Dobb’s J. Softw. Tools (2000)
Cao, Q., et al.: VGGFace2: A dataset for recognising faces across pose and age (2018). arXiv: 1710.08092 [cs.CV]
Chowdhry, D.A., et al.: Smart security system for sensitive area using face recognition. In: 2013 IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (CSUDET), pp. 11–14 (2013)
Django Contributors. Django 3.1 (2020). https://www.djangoproject.com/
DVC Contributors. Iterative, DVC: Data Version Control - Git for Data & Models (2020). https://doi.org/10.5281/zenodo.012345
Elasticsearch Contributors. Function Score query 6.8 (2019). https://www.elastic.co/guide/en/elasticsearch/reference/6.8/query-dslfunction-score-query.html
Elasticsearch Contributors. Rescoring 6.8 (2019). https://www.elastic.co/guide/en/elasticsearch/reference/6.8/search-request-rescore.html
Tensorflow Contributors. Tensorflow Serving 6.8 (2019). https://github.com/tensorflow/serving
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893 (2005)
Deb, D., Nain, N., Jain, A.K.: longitudinal study of child face recognition (2017). arXiv: 1711.03990 [cs.CV]
Deng, J., et al.: ArcFace: additive angular margin loss for deep face recognition (2019). arXiv: 1801.07698 [cs.CV]
Deng, J., et al.: RetinaFace: single-stage dense face localisation in the wild (2019). arXiv: 1905.00641 [cs.CV]
Karl Pearson, F.R.S.: LIII. On lines and planes of closest fit to systems of points in space. London Edinburgh Dublin Philos. Mag. J. Sci. 2(11), 559–572 (1901). https://doi.org/10.1080/14786440109462720
Girshick, R., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation (2014). arXiv: 1311.2524 [cs.CV]
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016). http://www.deeplearningbook.org
He, K., et al.: Deep residual learning for image recognition (2015). arXiv: 1512.03385 [cs.CV]
Hearst, M.A., et al.: Support vector machines. IEEE Intell. Syst. Their Appl. 13(4), 18–28 (1998)
Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications (2017). arXiv: 1704.04861 [cs.CV]
King, D.E.: Dlib-Ml: a machine learning toolkit. J. Mach. Learn. Res. 10, 1755–1758 (2009). ISSN: 1532-4435
Komulainen, J., Hadid, A., Pietikainen, M.: Context based face anti-spoofing, pp. 1–8, September 2013. https://doi.org/10.1109/BTAS.2013.6712690
Huang, G.B., Learned-Miller, E.: Labeled faces in the wild: updates and new reporting procedures. Technical report UM-CS-2014-003. University of Massachusetts, Amherst, May 2014
Li, H., et al.: A convolutional neural network cascade for face detection. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5325–5334 (2015)
Lienhart, R., Maydt, J.: An extended set of Haar-like features for rapid object detection. In: Proceedings of International Conference on Image Processing, vol. 1, p. I (2002)
Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2. ISBN 978-3-319-46447-3
Mu, C., et al.: Towards practical visual search engine within elasticsearch (2019). arXiv: 1806.08896 [cs.CV]
Ng, H.-W., Winkler, S.: A data-driven approach to cleaning large face datasets. In: 2014 IEEE International Conference on Image Processing, ICIP 2014, pp. 343–347, January 2015. https://doi.org/10.1109/ICIP.2014.7025068
Owayjan, M., et al.: Face recognition security system, December 2013
Parmar, D., Mehta, B.: Face recognition methods & applications. Int. J. Comput. Technol. Appl. 4, 84–86 (2014)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Rima, S., et al.: Smart security surveillance using IoT, pp. 659–663, August 2018. https://doi.org/10.1109/ICRITO.2018.8748703
Sahoo, D., et al.: Online deep learning: learning deep neural networks on the fly (2017). arXiv: 1711.03705 [cs.LG]
Satish, A., Devarajan, N.: Preprocessing technique for face recognition applications under varying illumination conditions. Glob. J. Comput. Sci. Technol. Graph. Vis. 12, 13–18 (2012)
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015. https://doi.org/10.1109/cvpr.2015.7298682. http://dx.doi.org/10.1109/CVPR.2015
Shi, Y., Otto, C., Jain, A.K.: Face clustering: representation and pairwise constraints. IEEE Trans. Inform. Forensics Secur. 13(7), 1626–1640 (2018). https://doi.org/10.1109/tifs.2018.2796999. https://dx.doi.org/10.1109/TIFS.2018
Taigman, Y., et al.: DeepFace: closing the gap to human-level performance in face verification, September 2014. https://doi.org/10.1109/CVPR.2014.220
Tang, X., et al.: PyramidBox: a context-assisted single shot face detector (2018). arXiv: 1803.07737 [cs.CV]
Tolba, A., El-Baz, A., El-Harby, A.: Face recognition: a literature review. Int. J. Signal Process. 2, 88–103 (2005)
Vikram, K., Padmavathi, S.: Facial parts detection using Viola Jones algorithm. In: 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 1–4 (2017)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features, vol. 1, p. I-511, February 2001. ISBN: 0-7695-1272-0. https://doi.org/10.1109/CVPR.2001.990517
Wang, H., et al.: CosFace: large margin cosine loss for deep face recognition (2018). arXiv: 1801.09414 [cs.CV]
Wang, M., Deng, W.: Deep face recognition: a survey (2018). arXiv: 1804.06655 [cs.CV]
Wang, Y., Yao, Q.: Few-shot learning: a survey. CoRR abs/1904.05046 (2019). arXiv: 1904.05046. http://arxiv.org/abs/1904.05046
Wold, S., Esbensen, K., Geladi, P.: Principal component analysis. Chemometr. Intell. Lab. Syst. 2(1), 37–52 (1987). Proceedings of the Multivariate Statistical Workshop for Geologists and Geochemists. ISSN 0169-7439. https://doi.org/10.1016/0169-7439(87)80084-9. http://www.sciencedirect.com/science/article/pii/0169743987800849
Wright, J., et al.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009). https://doi.org/10.1109/TPAMI.2008.79
Yang, H., et al.: An empirical study of recent face alignment methods, November 2015. https://doi.org/10.13140/RG.2.1.4603.8484
Yang, J., et al.: Nuclear norm based matrix regression with applications to face recognition with occlusion and illumination changes. IEEE Trans. Pattern Anal. Mach. Intell. 39(1), 156–171 (2017)
Yi, D., et al.: Learning face representation from scratch (2014). arXiv: 1411.7923 [cs.CV]
Zhang, K., et al.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23, 1499–1503 (2016). https://doi.org/10.1109/LSP.2016.2603342
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Van, D.N., Trung, S.N., Hong, A.P.T., Hoang, T.T., Thanh, T.M. (2021). A Novel Approach to End-to-End Facial Recognition Framework with Virtual Search Engine ElasticSearch. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12951. Springer, Cham. https://doi.org/10.1007/978-3-030-86970-0_32
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