Abstract
We propose a robust semi-auto system of labeling faces for characters in video by combining face detection, tracking, and recognition. At the very first step, our system detects the faces automatically in each video frame. After that, the face detection responses will be linked together to form face sequences (raw tracklets) in each video shot by employing simple temporal-spatial constraints to associate. Then we apply a tracking algorithm to not only extend those raw tracklets bi-directionally to cover more appearance views of the object instead of focusing only the frontal view, but also to help fixing the “gaps” caused by missed detection. After being merged among the potential overlapped ones, in the next step, these extended non-overlapped face tracklets across the video are associated with each other by our proposed Heuristics clustering algorithm. In order to achieve high accuracy, we use both generative and discriminative appearance models of the faces and also the context information, which is the clothing color feature in our case. An extensive experiment is performed on approximately 3.5 hours of videos cut from two TV series “Friends”, “How I Met Your Mother” to show the robustness of our system.
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Nguyen, HT., Nguyen, NH., Dinh, T.B., Dinh, T.B. (2013). Interactive Face Labeling System in Real-World Videos. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2013. ICCSA 2013. Lecture Notes in Computer Science, vol 7975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39640-3_23
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DOI: https://doi.org/10.1007/978-3-642-39640-3_23
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