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How good can a face identifier be without learning?
KTH, School of Computer Science and Communication (CSC), Media Technology and Interaction Design, MID.ORCID iD: 0000-0002-8673-0797
KTH, School of Computer Science and Communication (CSC), Media Technology and Interaction Design, MID.
KTH, School of Computer Science and Communication (CSC), Media Technology and Interaction Design, MID.ORCID iD: 0000-0003-3779-5647
2017 (English)In: 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2016, Springer, 2017, Vol. 693, p. 515-533Conference paper (Refereed)
Abstract [en]

Constructing discriminative features is an essential issue in developing face recognition algorithms. There are two schools in how features are constructed: hand-crafted features and learned features from data. A clear trend in the face recognition community is to use learned features to replace hand-crafted ones for face recognition, due to the superb performance achieved by learned features through Deep Learning networks. Given the negative aspects of database-dependent solutions, we consider an alternative and demonstrate that, for good generalization performance, developing face recognition algorithms by using hand-crafted features is surprisingly promising when the training dataset is small or medium sized. We show how to build such a face identifier with our Block Matching method which leverages the power of the Gabor phase in face images. Although no learning process is involved, empirical results show that the performance of this “designed” identifier is comparable (superior) to state-of-the-art identifiers and even close to Deep Learning approaches.

Place, publisher, year, edition, pages
Springer, 2017. Vol. 693, p. 515-533
Series
Communications in Computer and Information Science, ISSN 1865-0929 ; 693
Keywords [en]
Block matching, Controlled scenario, Deep learning, Face recognition, HD gabor phase, Learning-free
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-216323DOI: 10.1007/978-3-319-64870-5_25Scopus ID: 2-s2.0-85028327925ISBN: 9783319648699 (print)OAI: oai:DiVA.org:kth-216323DiVA, id: diva2:1153959
Conference
11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2016, Rome, Italy, 27 February 2016 through 29 February 2016
Note

QC 20171101

Available from: 2017-11-01 Created: 2017-11-01 Last updated: 2018-01-13Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf