summaryrefslogtreecommitdiff
path: root/datasets/scholar/entries/FDDB: A Benchmark for Face Detection in Unconstrained Settings.csv
diff options
context:
space:
mode:
authoradamhrv <adam@ahprojects.com>2018-11-04 21:54:00 +0100
committeradamhrv <adam@ahprojects.com>2018-11-04 21:54:00 +0100
commit9bcba0d02aafb34a5a9ca3db2f894f1fc95401c0 (patch)
tree3dcaf94563498c15b56d51efc62750d0be72e01a /datasets/scholar/entries/FDDB: A Benchmark for Face Detection in Unconstrained Settings.csv
parentef45f3c93ffd39b57ee56db74a95f9d2dae074a8 (diff)
parent0dc3e40434c23e4d48119465f39b03bf35fb56bd (diff)
.
Diffstat (limited to 'datasets/scholar/entries/FDDB: A Benchmark for Face Detection in Unconstrained Settings.csv')
-rw-r--r--datasets/scholar/entries/FDDB: A Benchmark for Face Detection in Unconstrained Settings.csv1
1 files changed, 1 insertions, 0 deletions
diff --git a/datasets/scholar/entries/FDDB: A Benchmark for Face Detection in Unconstrained Settings.csv b/datasets/scholar/entries/FDDB: A Benchmark for Face Detection in Unconstrained Settings.csv
new file mode 100644
index 00000000..eeb0adb1
--- /dev/null
+++ b/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 …