Paper Info
Title
Family Member Identification from Photo Collections
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.
Year
DOI
Venue
2015
10.1109/WACV.2015.136
WACV
Keywords
Field
DocType
photo collections,face recognition,unsupervised em-style joint inference algorithm,inference mechanisms,social roles,arbitrary images,family member identification,clothing,face,face detection,support vector machines
Computer vision,Facial recognition system,Inference,Computer science,Support vector machine,Artificial intelligence,Face detection,Probabilistic logic,Semantics,Visual appearance
Conference
ISSN
Citations 
PageRank 
2472-6737
2
0.39
References 
Authors
15
4
Name
Order
Citations
PageRank
Qieyun Dai121719.85
Peter Carr21379.32
Leonid Sigal32163124.33
Derek Hoiem44998302.66