NDLI: Is that you? Metric learning approaches for face identification

Content Provider	IEEE Xplore Digital Library
Author	Guillaumin, M. Verbeek, J. Schmid, C.
Copyright Year	2009
Description	Author affiliation: LEAR, INRIA Grenoble Laboratoire Jean Kuntzmann, France (Guillaumin, M.; Verbeek, J.; Schmid, C.)
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.
Starting Page	498
Ending Page	505
File Size	2499877
Page Count	8
File Format	PDF
ISBN	9781424444205
ISSN	15505499
DOI	10.1109/ICCV.2009.5459197
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-29
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Level set Image segmentation Kernel Active contours Computational complexity Pixel Biomedical computing Computer science Graph theory Optimization methods
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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