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Everything is in the Face? Represent Faces with Object Bank

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Computer Vision - ACCV 2014 Workshops (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9010))

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Abstract

Object Bank (OB) [1] has been recently proposed as an object-level image representation for high-level visual recognition. OB represents an image from its responses to many pre-trained object filters. While OB has been validated in general image recognition tasks, it might seem ridiculous to represent a face with OB. However, in this paper, we study this anti-intuitive potential and show how OB can well represent faces amazingly, which seems a proof of the saying that “Everything is in the face”. With OB representation, we achieve results better than many low-level features and even competitive to state-of-the-art methods on LFW dataset under unsupervised setting. We then show how we can achieve state of the art results by combining OB with some low-level feature (e.g. Gabor).

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Acknowledgement

The work is supported by Natural Science Foundation of China under contract No. 61222211.

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Authors and Affiliations

  1. Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, 100190, China

    Xin Liu, Shiguang Shan & Shaoxin Li

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Xin Liu & Shaoxin Li

  3. School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Alexander G. Hauptmann

Authors
  1. Xin Liu
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  2. Shiguang Shan
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  3. Shaoxin Li
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  4. Alexander G. Hauptmann
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Corresponding author

Correspondence to Shiguang Shan .

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Editors and Affiliations

  1. Center for Visual Information Technology, International Institute of Information Technology, Hyderabad, India

    C. V. Jawahar

  2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

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Liu, X., Shan, S., Li, S., Hauptmann, A.G. (2015). Everything is in the Face? Represent Faces with Object Bank. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9010. Springer, Cham. https://doi.org/10.1007/978-3-319-16634-6_14

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  • DOI: https://doi.org/10.1007/978-3-319-16634-6_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16633-9

  • Online ISBN: 978-3-319-16634-6

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