summaryrefslogtreecommitdiff
path: root/datasets/scholar/entries/300 faces In-the-wild challenge: Database and results.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/300 faces In-the-wild challenge: Database and results.csv
parentef45f3c93ffd39b57ee56db74a95f9d2dae074a8 (diff)
parent0dc3e40434c23e4d48119465f39b03bf35fb56bd (diff)
.
Diffstat (limited to 'datasets/scholar/entries/300 faces In-the-wild challenge: Database and results.csv')
-rw-r--r--datasets/scholar/entries/300 faces In-the-wild challenge: Database and results.csv1
1 files changed, 1 insertions, 0 deletions
diff --git a/datasets/scholar/entries/300 faces In-the-wild challenge: Database and results.csv b/datasets/scholar/entries/300 faces In-the-wild challenge: Database and results.csv
new file mode 100644
index 00000000..eaaf1a93
--- /dev/null
+++ b/datasets/scholar/entries/300 faces In-the-wild challenge: Database and results.csv
@@ -0,0 +1 @@
+300 faces in-the-wild challenge: Database and results|http://scholar.google.com/https://www.sciencedirect.com/science/article/pii/S0262885616000147|2016|141|9|4741451765657920988|None|http://scholar.google.com/scholar?cites=4741451765657920988&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=4741451765657920988&hl=en&as_sdt=0,5|None|Computer Vision has recently witnessed great research advance towards automatic facial points detection. Numerous methodologies have been proposed during the last few years that achieve accurate and efficient performance. However, fair comparison between these methodologies is infeasible mainly due to two issues.(a) Most existing databases, captured under both constrained and unconstrained (in-the-wild) conditions have been annotated using different mark-ups and, in most cases, the accuracy of the annotations is low.(b) Most …