From 640fb390baf494571114bc50b8059c9823ee899e Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Wed, 31 Oct 2018 02:15:35 +0100 Subject: data --- ... Database: A Benchmark for Face Recognition Under Wide Variations.csv | 1 + 1 file changed, 1 insertion(+) create mode 100644 datasets/scholar/entries/Indian Movie Face Database: A Benchmark for Face Recognition Under Wide Variations.csv (limited to 'datasets/scholar/entries/Indian Movie Face Database: A Benchmark for Face Recognition Under Wide Variations.csv') diff --git a/datasets/scholar/entries/Indian Movie Face Database: A Benchmark for Face Recognition Under Wide Variations.csv b/datasets/scholar/entries/Indian Movie Face Database: A Benchmark for Face Recognition Under Wide Variations.csv new file mode 100644 index 00000000..b9feb021 --- /dev/null +++ b/datasets/scholar/entries/Indian Movie Face Database: A Benchmark for Face Recognition Under Wide Variations.csv @@ -0,0 +1 @@ +Indian movie face database: a benchmark for face recognition under wide variations|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/6776225/|2013|29|7|10194316221634175118|None|http://scholar.google.com/scholar?cites=10194316221634175118&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=10194316221634175118&hl=en&as_sdt=0,5|None|Recognizing human faces in the wild is emerging as a critically important, and technically challenging computer vision problem. With a few notable exceptions, most previous works in the last several decades have focused on recognizing faces captured in a laboratory setting. However, with the introduction of databases such as LFW and Pubfigs, face recognition community is gradually shifting its focus on much more challenging unconstrained settings. Since its introduction, LFW verification benchmark is getting a lot of attention with various … -- cgit v1.2.3-70-g09d2