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Remarks on Computational Facial Expression Recognition from HOG Features Using Quaternion Multi-layer Neural Network

  • Kazuhiko Takahashi
  • Sae Takahashi
  • Yunduan Cui
  • Masafumi Hashimoto
Part of the Communications in Computer and Information Science book series (CCIS, volume 459)

Abstract

Facial expression recognition is an important technology in human-computer interaction. This study investigates a method for facial expression recognition using quaternion neural networks. A multi-layer quaternion neural network that conducts its learning using a quaternion back-propagation algorithm is employed to design the facial expression recognition system. The input feature vector of the recognition system is composed of histograms of oriented gradients calculated from an input facial expression image, and the output vector of the quaternion neural network indicates the class of facial expressions such as happiness, anger, sadness, fear, disgust, surprise and neutral. Computational experimental results show the feasibility of the proposed method for recognising human facial expressions.

Keywords

Quaternion neural network Facial expression recognition Histograms of oriented gradients Image processing 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Kazuhiko Takahashi
    • 1
  • Sae Takahashi
    • 1
  • Yunduan Cui
    • 2
  • Masafumi Hashimoto
    • 3
  1. 1.Information Systems DesignDoshisha UniversityKyotoJapan
  2. 2.Graduate School of Doshisha UniversityKyotoJapan
  3. 3.Intelligent Information Engineering and ScienceDoshisha UniversityKyotoJapan

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