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Head Roll Estimation Using Horizontal Energy Maximization

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Advanced Concepts for Intelligent Vision Systems (ACIVS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9386))

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Abstract

Head Pose estimation is often a necessary step for many applications using human face, for example in human-computer interaction systems, in face recognition or in face tracking. Here we present a new method to estimate face roll by maximizing a horizontal global energy. The main idea is face salient elements such as nose basis, eyes and mouth have an approximate horizontal direction. According to roll orientation, several local maximums are extracted. A further step of validation using a score computed on relative positions, sizes, and patterns of eyes, nose and mouth allow choosing one of the local maximums. This method is evaluated on BioID and Color Feret databases and achieves roll estimation with a mean absolute error of approximately \(4\deg \).

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Author information

Authors and Affiliations

  1. Université de Paris Descartes, LIPADE, 45 rue des Saints-Pères, 75006, Paris, France

    Nam-Jun Pyun & Nicole Vincent

  2. Konbini, 20 Rue du Faubourg du Temple, 75011, Paris, France

    Nam-Jun Pyun

Authors
  1. Nam-Jun Pyun
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  2. Nicole Vincent
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Corresponding author

Correspondence to Nam-Jun Pyun .

Editor information

Editors and Affiliations

  1. Dipartimento di Matematica e Informatica, Università di Catania, Catania, Catania, Italy

    Sebastiano Battiato

  2. Arcueil CX, France

    Jacques Blanc-Talon

  3. Catania, Italy

    Giovanni Gallo

  4. Gent, Belgium

    Wilfried Philips

  5. CSIRO, Sydney, New South Wales, Australia

    Dan Popescu

  6. Vision Lab., University of Antwerp, Antwerpen, Belgium

    Paul Scheunders

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© 2015 Springer International Publishing Switzerland

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Pyun, NJ., Vincent, N. (2015). Head Roll Estimation Using Horizontal Energy Maximization. In: Battiato, S., Blanc-Talon, J., Gallo, G., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2015. Lecture Notes in Computer Science(), vol 9386. Springer, Cham. https://doi.org/10.1007/978-3-319-25903-1_38

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  • DOI: https://doi.org/10.1007/978-3-319-25903-1_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25902-4

  • Online ISBN: 978-3-319-25903-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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