Abstract
In this paper, we propose a novel graph based approach for still-to-video based face recognition, in which the temporal and spatial information of the face from each frame of the video is utilized. The spatial information is incorporated using a graph based face representation. The graphs contain information on the appearance and geometry of facial feature points and are labeled using the feature descriptors of the feature points. The temporal information is captured using an adaptive probabilistic appearance model. The recognition is performed in two stages where in the first stage a Maximum a Posteriori solution based on PCA is computed to prune the search space and select fewer candidates. A simple deterministic algorithm which exploits the topology of the graph is used for matching in the second stage. The experimental results on the UTD database and our dataset show that the adaptive matching and the graph based representation provides robust performance in recognition.
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Mahalingam, G., Kambhamettu, C. (2011). Video Based Face Recognition Using Graph Matching. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6494. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19318-7_7
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DOI: https://doi.org/10.1007/978-3-642-19318-7_7
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