Abstract
Each facial event will give rise to complex facial appearance variation. In this paper, we propose similarity features to describe the facial appearance for video-based facial event analysis. Inspired by the kernel features, for each sample, we compare it with the reference set with a similarity function, and we take the log-weighted summarization of the similarities as its similarity feature. Due to the distinctness of the apex images of facial events, we use their cluster-centers as the references. In order to capture the temporal dynamics, we use the K-means algorithm to divide the similarity features into several clusters in temporal domain, and each cluster is modeled by a Gaussian distribution. Based on the Gaussian models, we further map the similarity features into dynamic binary patterns to handle the issue of time resolution, which embed the time-warping operation implicitly. The haar-like descriptor is used to extract the visual features of facial appearance, and Adaboost is performed to learn the final classifiers. Extensive experiments carried on the Cohn-Kanade database show the promising performance of the proposed method.
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Yang, P., Liu, Q., Metaxas, D. (2008). Similarity Features for Facial Event Analysis. In: Forsyth, D., Torr, P., Zisserman, A. (eds) Computer Vision – ECCV 2008. ECCV 2008. Lecture Notes in Computer Science, vol 5302. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88682-2_52
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DOI: https://doi.org/10.1007/978-3-540-88682-2_52
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