From ee3d0d98e19f1d8177d85af1866fd0ee431fe9ea Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Sun, 25 Nov 2018 22:19:15 +0100 Subject: moving stuff --- .../entries/A data-driven approach to cleaning large face datasets.csv | 1 + 1 file changed, 1 insertion(+) create mode 100644 scraper/datasets/scholar/entries/A data-driven approach to cleaning large face datasets.csv (limited to 'scraper/datasets/scholar/entries/A data-driven approach to cleaning large face datasets.csv') diff --git a/scraper/datasets/scholar/entries/A data-driven approach to cleaning large face datasets.csv b/scraper/datasets/scholar/entries/A data-driven approach to cleaning large face datasets.csv new file mode 100644 index 00000000..c1bf1f38 --- /dev/null +++ b/scraper/datasets/scholar/entries/A data-driven approach to cleaning large face datasets.csv @@ -0,0 +1 @@ +A data-driven approach to cleaning large face datasets|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/7025068/|2014|163|8|9390951279725836807|None|http://scholar.google.com/scholar?cites=9390951279725836807&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=9390951279725836807&hl=en&as_sdt=0,5|None|Large face datasets are important for advancing face recognition research, but they are tedious to build, because a lot of work has to go into cleaning the huge amount of raw data. To facilitate this task, we describe an approach to building face datasets that starts with detecting faces in images returned from searches for public figures on the Internet, followed by discarding those not belonging to each queried person. We formulate the problem of identifying the faces to be removed as a quadratic programming problem, which exploits the … -- cgit v1.2.3-70-g09d2