Abstract
In this paper, we propose the approach of structuring information in video surveillance systems by grouping the videos, which contain identical faces. First, the faces are detected in each frame and features of each facial region are extracted at the output of preliminarily trained deep convolution neural networks. Second, the tracks that contain identical faces are grouped using face verification algorithms and hierarchical agglomerative clustering. In the experimental study with the YTF dataset, we examined several ways to aggregate features of individual frame in order to obtain descriptor of the whole video track. It was demonstrated that the most accurate and fast algorithm is the matching of normalized average feature vectors.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Zhang, Y.J., Lu, H.B.: A hierarchical organization scheme for video data. Pattern Recogn. 35(11), 2381–2387 (2002)
Sokolova, A.D., Kharchevnikova, A.S., Savchenko, A.V.: Organizing multi-media data in video surveillance systems based on face verification with convolutional neural networks. arXiv preprint arXiv:1709.05675 (2017)
Chen, J.C., Ranjan, R., Kumar, A., Chen, C.H., Patel, V.M., Chellappa, R.: An end-to-end system for unconstrained face verification with deep convolutional neural networks. In: IEEE International Conference on Computer Vision Workshops, pp. 118–126 (2015)
Li, H., Hua, G., Shen, X., Lin, Z., Brandt, J.: Eigen-PEP for video face recognition. In: Cremers D., Reid I., Saito H., Yang M.H. (eds.) Asian Conference on Computer Vision. ACCV 2014. LNCS, vol. 9005, pp. 17–33. Springer, Cham (2014)
Savchenko, A.V.: Deep neural networks and maximum likelihood search for approximate nearest neighbor in video-based image recognition. Opt. Memory Neural Netw. (Information Optics) 26(2), 129136 (2017)
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley (2009)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT press (2016)
Parkhi, O.M., Vedaldi, A., Zisserman, A.: Deep face recognition. In: Proceedings of the British Machine Vision, pp. 6–17 (2015)
Wolf, L., Hassner, T., Maoz, I.: Face recognition in unconstrained videos with matched background similarity. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 529–534 (2011)
Szeliski, R.: Computer vision: algorithms and applications. Springer Science and Business Media (2010)
Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: Exploiting the circulant structure of tracking-by-detection with kernels. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) European Conference on Computer Vision (ECCV 2012). LNCS, vol. 7575, pp. 702–715. Springer, Berlin, Heidelberg (2012)
Savchenko, A.V.: Clustering and maximum likelihood search for efficient statistical classification with medium-sized databases. Opt. Lett. 11(2), 329–341 (2017)
Wu, X., He, R., Sun, Z.: A Lightened CNN for deep face representation. arXiv:1511.02683 (2015)
Seltman, H.J.: Experimental design and analysis. Carnegie Mellon University, Pittsburgh (2012)
Yang, J., Ren, P., Chen, D., Wen, F., Li, H., Hua, G.: Neural aggregation network for video face recognition, arXiv: 1603.05474 (2016)
Miech, A., Laptev, I., Sivic, J.: Learnable pooling with context gating for video classification. arXiv preprint arXiv:1706.06905 (2017)
Rassadin, A.G., Gruzdev, A.S., Savchenko, A.V.: Group-level emotion recognition using transfer learning from face identification. arXiv preprint arXiv:1709.01688. accepted at ACM ICMI (2017)
Savchenko, V.V.: Study of stationarity of the random time series using the principle of the information divergence minimum. Radiophys. Quantum Electron. 60(1), 81–87 (2017)
Jia, Y., et al. Caffe: Convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM international conference on Multimedia, pp. 675–678 (2014)
Kulis, B.: Metric learning: a survey. Found. Trends Mach. Learn. 5(4), 287–364 (2013)
Savchenko, A.V., Belova, N.S.: Statistical testing of segment homogeneity in classification of piecewise-regular objects. Int. J. Appl. Math. Comput. Sci. 25(4), 915–925 (2015)
Savchenko, A.V.: Maximum-likelihood approximate nearest neighbor method in real-time image recognition. Pattern Recogn. 61, 459–469 (2017)
Nikitin, M.Y., Konushin, V.S., Konushin, A.S.: Neural network model for video-based face recognition with frames quality assessment. Comput. Opt. 5, 732–742 (2017)
Acknowledgements
The work was conducted at Laboratory of Algorithms and Technologies for Network Analysis, National Research University Higher School of Economics and supported by RSF (Russian Science Foundation) grant 14-41-00039.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Sokolova, A.D., Savchenko, A.V. (2018). Cluster Analysis of Facial Video Data in Video Surveillance Systems Using Deep Learning. In: Kalyagin, V., Pardalos, P., Prokopyev, O., Utkina, I. (eds) Computational Aspects and Applications in Large-Scale Networks. NET 2016. Springer Proceedings in Mathematics & Statistics, vol 247. Springer, Cham. https://doi.org/10.1007/978-3-319-96247-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-96247-4_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-96246-7
Online ISBN: 978-3-319-96247-4
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)