Abstract
In this paper, single sample face recognition (SSFR) problem is addressed by introducing an adaptive biometric system within a modular architecture where one detector per target individual is proposed. For each detector, a face model is generated with the gallery face image and updated overtime. Sequential Karhunen-Loeve technique is applied to update the face model using representative face captures which are selected from the operational data by using reliable tracking trajectories. This process helps to induce intra-class variation of face appearance and improve representativeness of the face models. The effectiveness of the proposed method is detailed in security surveillance and user authentication using Chokepoint and FIA datasets in SSFR setting.
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Notes
- 1.
Face models refer to one or more facial captures (used for a template matching system) or a set of parameters estimated using the facial captures (used for a pattern classification system) of the target individuals who are enrolled to the watch-list gallery.
References
Puvvadi, U.L.N., Benedetto, K.D., Patil, A., Kang, K.-D., Park, Y.: Cost-effective security support in real-time video surveillance. IEEE Trans. Industr. Inf. 11(6), 1457–1465 (2015)
Tan, X., Chen, S., Zhou, Z.-H., Zhang, F.: Face recognition from a single image per person: a survey. Pattern Recogn. 39(9), 1725–1745 (2006)
Dewan, A., Granger, E., Roli, F., Sabourin, R., Marcialis, G.: Adaptive appearance model tracking for still-to-video face recognition. Pattern Recogn. 49, 129–151 (2016)
Ross, D.A., Lim, J., Lin, R.-S., Yang, M.-H.: Incremental learning for robust visual tracking. Int. J. Comput. Vis. 77(1), 125–141 (2008)
Levy, A., Lindenbaum, M.: Sequential Karhunen–Loeve basis extraction and its application to images. IEEE Trans. Image Process. 9(8), 1371–1374 (2000)
Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)
Zhang, T., Li, X., Guo, R.-Z.: Producing virtual face images for single sample face recognition. Optik-Int. J. Light Electron Opt. 125(17), 5017–5024 (2014)
Bashbagi, S., Granger, E., Sabourin, R., Bilodeau, G.-A.: Watch-list screening using ensembles based on multiple face representations. In: ICPR, Stockholm, Sweden (2014)
Wong, Y., Chen, S., Mau, S., Sanderson, C., Lovell, B.C.: Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition. In: CVPRW, Colorado, USA (2011)
Goh, R., Liu, L., Liu, X., Chen, T.: The CMU face in action database. In: AMFG, Beijing, China (2005)
Acknowledgement
This research is supported by Academic Research Fund and Research Incentive Grant, Athabasca University, and NSERC, Canada.
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Ali Akber Dewan, M., Qiao, D., Lin, F., Wen, D., Kinshuk (2016). An Approach to Improving Single Sample Face Recognition Using High Confident Tracking Trajectories. In: Khoury, R., Drummond, C. (eds) Advances in Artificial Intelligence. Canadian AI 2016. Lecture Notes in Computer Science(), vol 9673. Springer, Cham. https://doi.org/10.1007/978-3-319-34111-8_16
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DOI: https://doi.org/10.1007/978-3-319-34111-8_16
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