A Study for the Self Similarity Smile Detection

  • David Freire
  • Luis Antón
  • Modesto Castrillón
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5707)


Facial expression recognition has been the subject of much research in the last years within the Computer Vision community. The detection of smiles, however, has received less attention. Its distinctive configuration may pose less problem than other, at times subtle, expressions. On the other hand, smiles can still be very useful as a measure of happiness, enjoyment or even approval. Geometrical or local-based detection approaches like the use of lip edges may not be robust enough and thus researchers have focused on applying machine learning to appearance-based and self-similarity descriptors. This work makes an extensive experimental study of smile detection testing the Local Binary Patterns (LBP) combined with self similarity (LAC) as main descriptors of the image, along with the powerful Support Vector Machines classifier. Results show that error rates can be acceptable and the self similarity approach for the detection of smiles is suitable for real-time interaction, although there is still room for improvement.


Support Vector Machine Local Binary Pattern Facial Expression Recognition Computer Vision Community Generalize Linear Classi 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • David Freire
    • 1
  • Luis Antón
    • 1
  • Modesto Castrillón
    • 1
  1. 1.SIANI, Universidad de Las Palmas de Gran CanariaSpain

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