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/Localizing Parts of Faces Using a Consensus of Exemplars.csv | 1 + 1 file changed, 1 insertion(+) create mode 100644 scraper/datasets/scholar/entries/Localizing Parts of Faces Using a Consensus of Exemplars.csv (limited to 'scraper/datasets/scholar/entries/Localizing Parts of Faces Using a Consensus of Exemplars.csv') diff --git a/scraper/datasets/scholar/entries/Localizing Parts of Faces Using a Consensus of Exemplars.csv b/scraper/datasets/scholar/entries/Localizing Parts of Faces Using a Consensus of Exemplars.csv new file mode 100644 index 00000000..0fa7a800 --- /dev/null +++ b/scraper/datasets/scholar/entries/Localizing Parts of Faces Using a Consensus of Exemplars.csv @@ -0,0 +1 @@ +Localizing parts of faces using a consensus of exemplars|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/6412675/|2013|740|13|8801930631236620204|None|http://scholar.google.com/scholar?cites=8801930631236620204&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=8801930631236620204&hl=en&as_sdt=0,5|None|We present a novel approach to localizing parts in images of human faces. The approach combines the output of local detectors with a nonparametric set of global models for the part locations based on over 1,000 hand-labeled exemplar images. By assuming that the global models generate the part locations as hidden variables, we derive a Bayesian objective function. This function is optimized using a consensus of models for these hidden variables. The resulting localizer handles a much wider range of expression, pose, lighting, and … -- cgit v1.2.3-70-g09d2