Unconstrained Face Verification using Deep CNN Features

被引：0
|
作者：
Chen, Jun-Cheng [1 ]
Patel, Vishal M. [2 ]
Chellappa, Rama [1 ]
机构：
[1] Univ Maryland, College Pk, MD 20742 USA
[2] Rutgers State Univ, New Brunswick, NJ USA
来源：
2016 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2016) | 2016年
关键词：
PATTERNS;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this Iwo; we present an algorithm for unconstrained face verification based on deep convolutional features and evaluate it on the newly released IARPA Janus Benchmark A (LIB-A) dataset as well as on the traditional Labeled Face in the Wild (LFW) dataset. The IJB-A dataset includes real world unconstrained faces from 500 subjects with:hill pose and illumination variations which are much harder than the LFW and Youtube Face (YTF) datasets. The deep convolutional neural network (DCNN) is trained using the CASIAWebFace dataset. Results of experimental evaluations on the LIB-A and the LEW datasets are provided.
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页数：9
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