Abstract
Different from previous automatic but offline annotation systems, this paper studies automatic and online face annotation for personal videos/episodes of TV series considering Nearest Neighbourhood, LDA and SVM classification with Local Binary Patterns, Discrete Cosine Transform and Histogram of Oriented Gradients feature extraction methods in terms of their recognition accuracies and execution times. The best performing feature extraction method and the classifier pair is found out to be SVM classification with Discrete Cosine Transform features
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Yilmaztürk, M.C., Ulusoy, I., Cicekli, N.K. (2011). Analysis of Face Recognition Algorithms for Online and Automatic Annotation of Personal Videos. In: Gelenbe, E., Lent, R., Sakellari, G., Sacan, A., Toroslu, H., Yazici, A. (eds) Computer and Information Sciences. Lecture Notes in Electrical Engineering, vol 62. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9794-1_45
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DOI: https://doi.org/10.1007/978-90-481-9794-1_45
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