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Appearance-Based Smile Intensity Estimation by Cascaded Support Vector Machines

  • Keiji Shimada
  • Tetsu Matsukawa
  • Yoshihiro Noguchi
  • Takio Kurita
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6468)

Abstract

Facial expression recognition is one of the most challenging research area in the image recognition field and has been studied actively for a long time. Especially, we think that smile is important facial expression to communicate well between human beings and also between human and machines. Therefore, if we can detect smile and also estimate its intensity at low calculation cost and high accuracy, it will raise the possibility of inviting many new applications in the future. In this paper, we focus on smile in facial expressions and study feature extraction methods to detect a smile and estimate its intensity only by facial appearance information (Facial parts detection, not required). We use Local Intensity Histogram (LIH), Center-Symmetric Local Binary Pattern (CS-LBP) or features concatenated LIH and CS-LBP to train Support Vector Machine (SVM) for smile detection. Moreover, we construct SVM smile detector as a cascaded structure both to keep the performance and reduce the calculation cost, and estimate the smile intensity by posterior probability. As a consequence, we achieved both low calculation cost and high performance with practical images and we also implemented the proposed methods to the PC demonstration system.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Keiji Shimada
    • 1
  • Tetsu Matsukawa
    • 2
  • Yoshihiro Noguchi
    • 1
  • Takio Kurita
    • 3
  1. 1.Human Technology Research Institute, Advanced Industrial Science and TechnologyTsukubaJapan
  2. 2.Graduate School of Systems and Information EngineeringUniversity of TsukubaTsukubaJapan
  3. 3.Faculty of EngineeringHiroshima UniversityHigashi-HiroshimaJapan

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