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International Workshop on Human Behavior Understanding

HBU 2011: Human Behavior Understanding pp 52–61Cite as

Analyzing Facial Behavioral Features from Videos

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7065))

Abstract

Face analysis from videos can be approached using two different strategies, depending on whether the temporal information is used or not. The most straightforward strategy applies still image based techniques to some selected (or all) frames and then fuses the results over the sequence. In contrast, an emerging strategy consists of encoding both facial structure and dynamics through spatiotemporal representations. To gain insight into the usefulness of facial dynamics, this paper considers two baseline systems and compares static versus spatiotemporal approaches to face analysis from videos. The first approach is based only on static images and uses spatial Local Binary Pattern features as inputs to SVM classifiers, while the second baseline system combines facial appearance and motion through a spatiotemporal representation using Volume LBP features as inputs to SVM classifiers. Preliminary experiments on classifying face patterns into different categories based on gender, identity, age, and ethnicity point out very interesting findings on the role of facial dynamics in face analysis from videos.

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Author information

Authors and Affiliations

  1. Machine Vision Group, University of Oulu, P.O. Box 4500, FI-90014, Finland

    Abdenour Hadid

Authors
  1. Abdenour Hadid
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Editor information

Editors and Affiliations

  1. Department of Computer Engineering, Bogaziçi University, 34342, Bebek, Istanbul, Turkey

    Albert Ali Salah

  2. FBK - Fondazione Bruno Kessler, Via Sommarive 18, 38100, Trento, Italy

    Bruno Lepri

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© 2011 Springer-Verlag Berlin Heidelberg

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Hadid, A. (2011). Analyzing Facial Behavioral Features from Videos. In: Salah, A.A., Lepri, B. (eds) Human Behavior Understanding. HBU 2011. Lecture Notes in Computer Science, vol 7065. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25446-8_6

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  • DOI: https://doi.org/10.1007/978-3-642-25446-8_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25445-1

  • Online ISBN: 978-3-642-25446-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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