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Comparison of Angle and Size Features with Deep Learning for Emotion Recognition

  • Patrick DunauEmail author
  • Marco F. Huber
  • Jürgen Beyerer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)

Abstract

The robust recognition of a person’s emotion from images is an important task in human-machine interaction. This task can be considered a classification problem, for which a plethora of methods exists. In this paper, the emotion recognition performance of two fundamentally different approaches is compared: classification based on hand-crafted features against deep learning. This comparison is conducted by means of well-established datasets and highlights the benefits and drawbacks of each approach.

Keywords

Emotion recognition Classification Deep learning 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Patrick Dunau
    • 1
    • 5
    Email author
  • Marco F. Huber
    • 2
    • 3
  • Jürgen Beyerer
    • 4
    • 5
  1. 1.USU Software AGKarlsruheGermany
  2. 2.Institute of Industrial Manufacturing and Management (IFF)University of StuttgartStuttgartGermany
  3. 3.Fraunhofer IPAStuttgartGermany
  4. 4.Fraunhofer IOSBKarlsruheGermany
  5. 5.Karlsruhe Institute of Technology (KIT)KarlsruheGermany

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