Improved Performance in Facial Expression Recognition Using 32 Geometric Features

  • Giuseppe PalestraEmail author
  • Adriana Pettinicchio
  • Marco Del Coco
  • Pierluigi Carcagnì
  • Marco Leo
  • Cosimo Distante
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9280)


Automatic facial expression recognition is one of the most interesting problem as it impacts on important applications in human-computer interaction area. Many applications in this field require real-time performance but not all the approach are suitable to satisfy this requirement. Geometrical features are usually the most light in terms of computational load but sometimes they exploits a huge number of features and do not cover all the possible geometrical aspect. In order to face up this problem, we propose an automatic pipeline for facial expression recognition that exploits a new set of 32 geometric facial features from a single face side covering a wide set of geometrical peculiarities. As a results, the proposed approach showed a facial expression recognition accuracy of 95,46% with a six-class expression set and an accuracy of 94,24% with a seven-class expression set.


Facial expression recognition Human-computer interaction Geometric features Random forest 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Giuseppe Palestra
    • 1
    Email author
  • Adriana Pettinicchio
    • 2
  • Marco Del Coco
    • 2
  • Pierluigi Carcagnì
    • 2
  • Marco Leo
    • 2
  • Cosimo Distante
    • 2
  1. 1.Department of Computer ScienceUniversity of BariBariItaly
  2. 2.National Institute of OpticsNational Research CouncilArnesanoItaly

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