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Evaluation of Face Datasets as Tools for Assessing the Performance of Face Recognition Methods

Authors
  • Shamir, Lior1
  • 1 National Institutes of Health, Laboratory of Genetics, National Institute on Aging, 333 Cassell Dr., Baltimore, MD, 21224, USA , Baltimore (United States)
Type
Published Article
Journal
International Journal of Computer Vision
Publisher
Springer-Verlag
Publication Date
May 15, 2008
Volume
79
Issue
3
Identifiers
DOI: 10.1007/s11263-008-0143-7
Source
Springer Nature
Keywords
License
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Abstract

Face datasets are considered a primary tool for evaluating the efficacy of face recognition methods. Here we show that in many of the commonly used face datasets, face images can be recognized accurately at a rate significantly higher than random even when no face, hair or clothes features appear in the image. The experiments were done by cutting a small background area from each face image, so that each face dataset provided a new image dataset which included only seemingly blank images. Then, an image classification method was used in order to check the classification accuracy. Experimental results show that the classification accuracy ranged between 13.5% (color FERET) to 99% (YaleB). These results indicate that the performance of face recognition methods measured using face image datasets may be biased. Compilable source code used for this experiment is freely available for download via the Internet.

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