An End-to-End System for Unconstrained Face Verification with Deep Convolutional Neural Networks

Jun Cheng Chen, Rajeev Ranjan, Amit Kumar, Ching Hui Chen, Vishal M. Patel, Rama Chellappa

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

14 Scopus citations

Abstract

In this paper, we present an end-to-end system for the unconstrained face verification problem based on deep convolutional neural networks (DCNN). The end-to-end system consists of three modules for face detection, alignment and verification and is evaluated using the newly released IARPA Janus Benchmark A (IJB-A) dataset and its extended version Janus Challenging set 2 (JANUS CS2) dataset. The IJB-A and CS2 datasets include real-world unconstrained faces of 500 subjects with significant pose and illumination variations which are much harder than the Labeled Faces in the Wild (LFW) and Youtube Face (YTF) datasets. Results of experimental evaluations for the proposed system on the IJB-A dataset are provided.

Original languageEnglish (US)
Title of host publicationProceedings - 2015 IEEE International Conference on Computer Vision Workshops, ICCVW 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages360-368
Number of pages9
ISBN (Electronic)9781467383905
DOIs
StatePublished - Feb 11 2016
Event15th IEEE International Conference on Computer Vision Workshops, ICCVW 2015 - Santiago, Chile
Duration: Dec 11 2015Dec 18 2015

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
Volume2016-February
ISSN (Print)1550-5499

Other

Other15th IEEE International Conference on Computer Vision Workshops, ICCVW 2015
Country/TerritoryChile
CitySantiago
Period12/11/1512/18/15

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

Keywords

  • Face
  • Face detection
  • Feature extraction
  • Lighting
  • Neural networks
  • Tracking
  • Videos

Fingerprint

Dive into the research topics of 'An End-to-End System for Unconstrained Face Verification with Deep Convolutional Neural Networks'. Together they form a unique fingerprint.

Cite this