From 93b3392d9346226c328ea2a878ff968d0303f826 Mon Sep 17 00:00:00 2001 From: "jules@lens" Date: Wed, 31 Oct 2018 02:14:14 +0100 Subject: data --- ...e Age-Gap Face Verification by Feature Injection in Deep Networks.csv | 1 + 1 file changed, 1 insertion(+) create mode 100644 datasets/scholar/entries/Large Age-Gap Face Verification by Feature Injection in Deep Networks.csv (limited to 'datasets/scholar/entries/Large Age-Gap Face Verification by Feature Injection in Deep Networks.csv') diff --git a/datasets/scholar/entries/Large Age-Gap Face Verification by Feature Injection in Deep Networks.csv b/datasets/scholar/entries/Large Age-Gap Face Verification by Feature Injection in Deep Networks.csv new file mode 100644 index 00000000..9cd388eb --- /dev/null +++ b/datasets/scholar/entries/Large Age-Gap Face Verification by Feature Injection in Deep Networks.csv @@ -0,0 +1 @@ +Large age-gap face verification by feature injection in deep networks|http://scholar.google.com/https://www.sciencedirect.com/science/article/pii/S0167865517300727|2017|12|8|6980699793307007950|None|http://scholar.google.com/scholar?cites=6980699793307007950&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=6980699793307007950&hl=en&as_sdt=0,5|None|This paper introduces a new method for face verification across large age gaps and also a dataset containing variations of age in the wild, the Large Age-Gap (LAG) dataset, with images ranging from child/young to adult/old. The proposed method exploits a deep convolutional neural network (DCNN) pre-trained for the face recognition task on a large dataset and then fine-tuned for the large age-gap face verification task. Fine-tuning is performed in a Siamese architecture using a contrastive loss function. A feature injection … -- cgit v1.2.3-70-g09d2