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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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.
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Acknowledgement
This research is supported by Academic Research Fund and Research Incentive Grant, Athabasca University, and NSERC, Canada.
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© 2016 Springer International Publishing Switzerland
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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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