Abstract
This paper presents an analysis of datasets of images of human faces with annotated facial keypoints, which are important in human-machine interaction, and their comparison. Datasets are divided according to external conditions of the subject into two groups: datasets in laboratory conditions and in the wild data. Moreover, a quick review of the state-of-the-art methods for keypoints detection is provided. Existing methods are categorized into the following three groups according to the approach to the solution of the problem: top-down, bottom-up and their combination.
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Acknowledgments
This work is supported by grant of the University of West Bohemia, project No. SGS-2016-039, by Ministry of Education, Youth and Sports of Czech Republic, project No. LO1506, by Russian Foundation for Basic Research, project No. 15-07-04415, and by the Government of Russian, grant No. 074-U01.
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Gruber, I., Hlaváč, M., Hrúz, M., Železný, M., Karpov, A. (2016). An Analysis of Visual Faces Datasets. In: Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds) Interactive Collaborative Robotics. ICR 2016. Lecture Notes in Computer Science(), vol 9812. Springer, Cham. https://doi.org/10.1007/978-3-319-43955-6_3
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