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Human Facial Expression Recognition Using Hybrid Network of PCA and RBFN

  • Daw-Tung Lin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)

Abstract

In this paper, we propose a hybrid architecture combining radial basis function network (RBFN) and Principal Component Analysis (PCA) re-constructure model to perform facial expression recognition from static images. The resultant framework is a two stages coarse to fine discrimination model based on local features extracted from eyes and face images by applying PCA technique . It decomposes the acquired data into a small set of characteristic features. The objective of this research is to develop a more efficient approach to classify between seven prototypic facial expressions, such as neutral, joy, anger, surprise, fear, disgust, and sadness. A constructive procedure is detailed and the system performance is evaluated on a public database ”Japanese Females Facial Expression (JAFFE)”. As anticipated, the experimental results demonstrate the potential capabilities of the proposed approach.

Keywords

Facial Expression Emotion Recognition Radial Basis Function Neural Network Radial Basis Function Neural Network Facial Expression Recognition 
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 2006

Authors and Affiliations

  • Daw-Tung Lin
    • 1
  1. 1.Department of Computer Science and Information EngineeringNational Taipei UniversitySanshia, Taipei CountyTaiwan

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