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diff --git a/scraper/datasets/scholar/entries/A semi-automatic methodology for facial landmark annotation.csv b/scraper/datasets/scholar/entries/A semi-automatic methodology for facial landmark annotation.csv new file mode 100644 index 00000000..31bf7b39 --- /dev/null +++ b/scraper/datasets/scholar/entries/A semi-automatic methodology for facial landmark annotation.csv @@ -0,0 +1 @@ +A semi-automatic methodology for facial landmark annotation|http://scholar.google.com/https://www.cv-foundation.org/openaccess/content_cvpr_workshops_2013/W16/papers/Sagonas_A_Semi-automatic_Methodology_2013_CVPR_paper.pdf|2013|225|16|15744661091744891|http://scholar.google.com/https://www.cv-foundation.org/openaccess/content_cvpr_workshops_2013/W16/papers/Sagonas_A_Semi-automatic_Methodology_2013_CVPR_paper.pdf|http://scholar.google.com/scholar?cites=15744661091744891&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=15744661091744891&hl=en&as_sdt=0,5|None|Developing powerful deformable face models requires massive, annotated face databases on which techniques can be trained, validated and tested. Manual annotation of each facial image in terms of landmarks requires a trained expert and the workload is usually enormous. Fatigue is one of the reasons that in some cases annotations are inaccurate. This is why, the majority of existing facial databases provide annotations for a relatively small subset of the training images. Furthermore, there is hardly any correspondence between the … |