Video face recognition from a single still image using an adaptive appearance model tracker

Dewan, M. Ali Akber, Granger, E., Sabourin, R., Marcialis, G. L. and Roli, F.. 2015. « Video face recognition from a single still image using an adaptive appearance model tracker ». In 2015 IEEE Symposium Series on Computational Intelligence (Cape Town, South Africa, Dec. 7-10, 2015) pp. 196-202. IEEE.

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Abstract

Systems for still-to-video face recognition (FR) are typically used to detect target individuals in watch-list screening applications. These surveillance applications are challenging because the appearance of faces changes according to capture conditions, and very few reference stills are available a priori for enrollment. To improve performance, an adaptive appearance model tracker (AAMT) is proposed for on-line learning of a track-face-model linked to each individual appearing in the scene. Meanwhile, these models are matched over successive frames against stored gallery-face-models, extracted from reference still images of each target individual (enrolled to the system) for robust spatiotemporal FR. In addition, compared to the gallery-face-models produced by selfupdating FR systems, the track-face-models (produced by the AAMT-FR system) are updated from facial captures that are more reliably selected, and can incorporate greater intra-class variations from the operational environment. Track-facemodels allow selecting facial captures for modeling more reliably than self-updating FR systems, and can incorporate a greater diversity of intra-class variation from the operational environment. Performance of the proposed approach is compared with several state-of-the-art FR systems on videos from the Chokepoint dataset when a single reference template per target individual is stored in the gallery. Experimental results show that the proposed system can achieve a significantly higher level of FR performance, especially when the diverse facial appearances captured through AAMT correspond to that of reference stills.

Item Type:	Conference proceeding
Professor:	Professor Granger, Éric Sabourin, Robert
Affiliation:	Génie de la production automatisée, Génie de la production automatisée
Date Deposited:	03 Feb 2016 20:36
Last Modified:	22 Aug 2018 20:18
URI:	https://espace2.etsmtl.ca/id/eprint/12258

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