The Use of Various Statistical Co-occurrence Patterns for Boosting Face Tagging
Date Issued
2014
Date
2014
Author(s)
Hsu, Yi-Ping
Abstract
Face recognition benefits many applications such as photo searching and
annotation. However, such technique is not robust enough to identify people
under widely varying photo conditions. In this paper, we propose a semisupervised
probabilistic graphical model for boosting face recognition by predicting
the names of the unrecognized faces in a statistical manner. Different
from pairwise co-occurrence context adopted by conventional approaches, we
explore more useful context such as the co-occurrence between subgroups,
the different frequency of occurrence for people, and the different numbers of
people appearing in photos, and integrate them into our model. Experiments
on Facebook and Picasa demonstrate that the proposed model can effectively
improve the recognition performance, compared to both of unsupervised and
supervised learning methods.
Subjects
標記
人臉標記
標籤推薦
人臉辨識
Type
thesis
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