Frontal to profile face verification in the wild

Soumyadip Sengupta, Jun Cheng Chen, Carlos Castillo, Vishal M. Patel, Rama Chellappa, David W. Jacobs

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

200 Scopus citations

Abstract

We have collected a new face data set that will facilitate research in the problem of frontal to profile face verification 'in the wild'. The aim of this data set is to isolate the factor of pose variation in terms of extreme poses like profile, where many features are occluded, along with other 'in the wild' variations. We call this data set the Celebrities in Frontal-Profile (CFP) data set. We find that human performance on Frontal-Profile verification in this data set is only slightly worse (94.57% accuracy) than that on Frontal-Frontal verification (96.24% accuracy). However we evaluated many state-of-the-art algorithms, including Fisher Vector, Sub-SML and a Deep learning algorithm. We observe that all of them degrade more than 10% from Frontal-Frontal to Frontal-Profile verification. The Deep learning implementation, which performs comparable to humans on Frontal-Frontal, performs significantly worse (84.91% accuracy) on Frontal-Profile. This suggests that there is a gap between human performance and automatic face recognition methods for large pose variation in unconstrained images.

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
Country/TerritoryUnited States
CityLake Placid
Period3/7/163/10/16

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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