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      FAME: Face association through model evolution

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      Author
      Gölge, Eren
      Duygulu, Pınar
      Date
      2015-06
      Source Title
      IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
      Publisher
      IEEE
      Pages
      43 - 49
      Language
      English
      Type
      Conference Paper
      Item Usage Stats
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      Abstract
      We attack the problem of building classifiers for public faces from web images collected through querying a name. The search results are very noisy even after face detection, with several irrelevant faces corresponding to other people. Moreover, the photographs are taken in the wild with large variety in poses and expressions. We propose a novel method, Face Association through Model Evolution (FAME), that is able to prune the data in an iterative way, for the models associated to a name to evolve. The idea is based on capturing discriminative and representative properties of each instance and eliminating the outliers. The final models are used to classify faces on novel datasets with different characteristics. On benchmark datasets, our results are comparable to or better than the state-of-the-art studies for the task of face identification. © 2015 IEEE.
      Keywords
      Accuracy
      Buildings
      Computational modeling
      Data models
      Face
      Noise measurement
      Training
      Buildings
      Classification (of information)
      Data structures
      Face recognition
      Iterative methods
      Pattern recognition
      Personnel training
      Accuracy
      Benchmark datasets
      Computational model
      Face
      Face identification
      Model evolution
      Noise measurements
      State of the art
      Computer vision
      Permalink
      http://hdl.handle.net/11693/28185
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/CVPRW.2015.7301353
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      • Department of Computer Engineering 1368
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