From ee3d0d98e19f1d8177d85af1866fd0ee431fe9ea Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Sun, 25 Nov 2018 22:19:15 +0100 Subject: moving stuff --- .../FDDB: A Benchmark for Face Detection in Unconstrained Settings.csv | 1 + 1 file changed, 1 insertion(+) create mode 100644 scraper/datasets/scholar/entries/FDDB: A Benchmark for Face Detection in Unconstrained Settings.csv (limited to 'scraper/datasets/scholar/entries/FDDB: A Benchmark for Face Detection in Unconstrained Settings.csv') diff --git a/scraper/datasets/scholar/entries/FDDB: A Benchmark for Face Detection in Unconstrained Settings.csv b/scraper/datasets/scholar/entries/FDDB: A Benchmark for Face Detection in Unconstrained Settings.csv new file mode 100644 index 00000000..eeb0adb1 --- /dev/null +++ b/scraper/datasets/scholar/entries/FDDB: A Benchmark for Face Detection in Unconstrained Settings.csv @@ -0,0 +1 @@ +Fddb: A benchmark for face detection in unconstrained settings|http://www.cs.umass.edu/~elm/papers/fddb.pdf|2010|525|13|17267836250801810690|http://www.cs.umass.edu/~elm/papers/fddb.pdf|http://scholar.google.com/scholar?cites=17267836250801810690&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=17267836250801810690&hl=en&as_sdt=0,5|None|Despite the maturity of face detection research, it remains difficult to compare different algorithms for face detection. This is partly due to the lack of common evaluation schemes. Also, existing data sets for evaluating face detection algorithms do not capture some aspects of face appearances that are manifested in real-world scenarios. In this work, we address both of these issues. We present a new data set of face images with more faces and more accurate annotations for face regions than in previous data sets. We also propose two … -- cgit v1.2.3-70-g09d2