Abstract
This paper presents a semi-automatic method for creating a large scale facial key-point dataset from a small number of annotated images. The method consists of annotating the facial images by hand, training Active Appearance Model (AAM) from the annotated images and then using the AAM to annotate a large number of additional images for the purpose of training a neural network. The images from the AAM are then re-annotated by the neural network and used to validate the precision of the proposed neural network detections. The neural network architecture is presented including the training parameters.
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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, projects No. 15-07-04415 and 16-37-60100, and by the Government of Russia, grant No. 074-U01. Computational resources were supplied by the Ministry of Education, Youth and Sports of the Czech Republic under the Projects CESNET (Project No. LM2015042) and CERIT-Scientific Cloud (Project No. LM2015085) provided within the program Projects of Large Research, Development and Innovations Infrastructures.
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Hlaváč, M., Gruber, I., Železný, M., Karpov, A. (2017). Semi-automatic Facial Key-Point Dataset Creation. In: Karpov, A., Potapova, R., Mporas, I. (eds) Speech and Computer. SPECOM 2017. Lecture Notes in Computer Science(), vol 10458. Springer, Cham. https://doi.org/10.1007/978-3-319-66429-3_66
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DOI: https://doi.org/10.1007/978-3-319-66429-3_66
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