Unconstrained face verification using deep CNN features

Jun Cheng Chen, Vishal M. Patel, Rama Chellappa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

144 Scopus citations

Abstract

In this paper, we present an algorithm for unconstrained face verification based on deep convolutional features and evaluate it on the newly released IARPA Janus Benchmark A (IJB-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 full 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 CASIA-WebFace dataset. Results of experimental evaluations on the IJB-A and the LFW datasets are provided.

Original languageEnglish (US)
Title of host publication2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509006410
DOIs
StatePublished - May 23 2016
EventIEEE Winter Conference on Applications of Computer Vision, WACV 2016 - Lake Placid, United States
Duration: Mar 7 2016Mar 10 2016

Publication series

Name2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016

Other

OtherIEEE Winter Conference on Applications of Computer Vision, WACV 2016
CountryUnited States
CityLake Placid
Period3/7/163/10/16

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

Fingerprint Dive into the research topics of 'Unconstrained face verification using deep CNN features'. Together they form a unique fingerprint.

Cite this