Family member identification from photo collections

Qieyun Dai, Peter Carr, Leonid Sigal, Derek W Hoiem

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

Abstract

Family photo collections often contain richer semantics than arbitrary images of people because families contain a handful of specific individuals who can be associated with certain social roles (e.g. father, mother, or child). As a result, family photo collections have unique challenges and opportunities for face recognition compared to random groups of photos containing people. We address the problem of unsupervised family member discovery: given a collection of family photos, we infer the size of the family, as well as the visual appearance and social role of each family member. As a result, we are able to recognize the same individual across many different photos. We propose an unsupervised EM-style joint inference algorithm with a probabilistic CRF that models identity and role assignments for all detected faces, along with associated pair wise relationships between them. Our experiments illustrate how joint inference of both identity and role (across all photos simultaneously) outperforms independent estimates of each. Joint inference also improves the ability to recognize the same individual across many different photos.

Original languageEnglish (US)
Title of host publicationProceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages982-989
Number of pages8
ISBN (Electronic)9781479966820
DOIs
StatePublished - Feb 19 2015
Event2015 15th IEEE Winter Conference on Applications of Computer Vision, WACV 2015 - Waikoloa, United States
Duration: Jan 5 2015Jan 9 2015

Publication series

NameProceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015

Other

Other2015 15th IEEE Winter Conference on Applications of Computer Vision, WACV 2015
CountryUnited States
CityWaikoloa
Period1/5/151/9/15

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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