An experimental study on content-based face annotation of photos

  • Authors:

  • Mei-Chen Yeh;Sheng Zhang;Kwang-Ting Cheng

  • Affiliations:

  • Computer Science and Information Engineering Department, National Taiwan Normal University, Taipei, Taiwan;Psychology Department, University of California, Santa Barbara, CA;Electrical and Computer Engineering Department, University of California, Santa Barbara, CA

  • Venue:
  • BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
  • Year:
  • 2009

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Abstract

Face annotation of photos, a key enabling technology for many exciting new applications, has been gaining broad interest. The task is different from the general face recognition problem because the dataset is not constrained--an unlabelled face may not have any corresponding match in the training set. Moreover, faces in real-life photos have a significantly wider variation range than those in the conventional face datasets. We designed and conducted a thorough experimental study to understand the efficacy of face recognition methods for annotating faces in real-world scenarios. The findings of this study should provide information for various design choices for a practical and high-accuracy face annotation system.