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Efficient Detection of Consecutive Facial Expression Apices Using Biologically Based Log-Normal Filters

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Advances in Visual Computing (ISVC 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6938))

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Abstract

The automatic extraction of the most relevant information in a video sequence made of continuous affective states is an important challenge for efficient human-machine interaction systems. In this paper a method is proposed to solve this problem based on two steps: first, the automatic segmentation of consecutive emotional segments based on the response of a set of Log-Normal filters; secondly, the automatic detection of the facial expression apices based on the estimation of the global face energy inside each emotional segment independently of the undergoing facial expression. The proposed method is fully automatic and independent from any reference image such as the neutral at the beginning of the sequence. The proposed method is the first contribution for the summary of the most important affective information present in a video sequence independently of the undergoing facial expressions. The robustness and efficiency of the proposed method to different data acquisition and facial differences has been evaluated on a large set of data (157 video sequences) taken from two benchmark databases (Hammal-Caplier and MMI databases) [1, 2] and from 20 recorded video sequences of multiple facial expressions (between three to seven facial expressions per sequence) in order to include more challenging image data in which expressions are not neatly packaged in neutral-expression-neutral.

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Author information

Authors and Affiliations

  1. The Robotics institute of the Carnegie Mellon University CMU, 4323 Sennott Square, 210 S. Bouquet Street, Pittsburgh, PA, 15260, USA

    Zakia Hammal

Authors
  1. Zakia Hammal
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Editor information

Editors and Affiliations

  1. University of Nevada, 89557, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, 94035, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, 94720, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, 89512, Reno, NV, USA

    Darko Koracin

  5. Department of Computer Science and Engineering, University of South Carolina, 29208, Columbia, SC, USA

    Song Wang

  6. HRL Laboratories, 3011 Malibu Canyon Road, 90265-4797, Malibu, CA, USA

    Kim Kyungnam

  7. Purdue University, 47907-2021, West Lafayette, IN, USA

    Bedrich Benes

  8. Sandia National Laboratory, 87185, Albuquerque, NM, USA

    Kenneth Moreland

  9. University of Louisiana, 70504, Lafayette, LA, USA

    Christoph Borst

  10. Adobe Systems Incorporated, San Francisco, CA, USA

    Stephen DiVerdi

  11. Polytechnic Institute of NYU, 11201, Brooklyn, NY, USA

    Chiang Yi-Jen

  12. Lawrence Livermore National Laboratory, 94551-0808, Livermore, CA, USA

    Jiang Ming

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© 2011 Springer-Verlag Berlin Heidelberg

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Hammal, Z. (2011). Efficient Detection of Consecutive Facial Expression Apices Using Biologically Based Log-Normal Filters. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2011. Lecture Notes in Computer Science, vol 6938. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24028-7_54

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  • DOI: https://doi.org/10.1007/978-3-642-24028-7_54

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24027-0

  • Online ISBN: 978-3-642-24028-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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