DMCA
Is that you? Metric learning approaches for face identification (2009)
Cached
Download Links
| Venue: | In ICCV |
| Citations: | 159 - 8 self |
BibTeX
@INPROCEEDINGS{Guillaumin09isthat,
author = {Matthieu Guillaumin and Jakob Verbeek and Cordelia Schmid},
title = {Is that you? Metric learning approaches for face identification},
booktitle = {In ICCV},
year = {2009}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Face identification is the problem of determining whether two face images depict the same person or not. This is difficult due to variations in scale, pose, lighting, background, expression, hairstyle, and glasses. In this paper we present two methods for learning robust distance measures: (a) a logistic discriminant approach which learns the metric from a set of labelled image pairs (LDML) and (b) a nearest neighbour approach which computes the probability for two images to belong to the same class (MkNN). We evaluate our approaches on the Labeled Faces in the Wild data set, a large and very challenging data set of faces from Yahoo! News. The evaluation protocol for this data set defines a restricted setting, where a fixed set of positive and negative image pairs is given, as well as an unrestricted one, where faces are labelled by their identity. We are the first to present results for the unrestricted setting, and show that our methods benefit from this richer training data, much more so than the current state-of-the-art method. Our results of 79.3 % and 87.5 % correct for the restricted and unrestricted setting respectively, significantly improve over the current state-of-the-art result of 78.5%. Confidence scores obtained for face identification can be used for many applications e.g. clustering or recognition from a single training example. We show that our learned metrics also improve performance for these tasks. 1.
Keyphrases
face identification unrestricted setting learned metric many application labelled image pair fixed set yahoo news negative image pair current state-of-the-art result evaluation protocol labeled face wild data set present result robust distance measure logistic discriminant approach face image training data restricted setting neighbour approach confidence score current state-of-the-art method single training example data set challenging data set
