Face Expression Recognition Using Histograms of Oriented Gradients with Reduced Features

  • Nikunja Bihari KarEmail author
  • Korra Sathya Babu
  • Sanjay Kumar Jena
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 460)


Facial expression recognition has been an emerging research area in last two decades. This paper proposes a new hybrid system for automatic facial expression recognition. The proposed method utilizes histograms of oriented gradients (HOG) descriptor to extract features from expressive facial images. Feature reduction techniques namely principal component analysis (PCA) and linear discriminant analysis (LDA) are applied to obtain the most important discriminant features. Finally, the discriminant features are fed to the back-propagation neural network (BPNN) classifier to determine the underlying emotions from expressive facial images. The Extended Cohn-Kanade dataset (CK\(+\)) is used to validate the proposed method. Experimental results indicate that the proposed system provides the better result as compared to state-of-the-art methods in terms of accuracy with the substantially lesser number of features.


Face expression recognition Histograms of oriented gradients Principal component analysis Linear discriminant analysis BPNN 


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Copyright information

© Springer Science+Business Media Singapore 2017

Authors and Affiliations

  • Nikunja Bihari Kar
    • 1
    Email author
  • Korra Sathya Babu
    • 1
  • Sanjay Kumar Jena
    • 1
  1. 1.Department of Computer Science and EngineeringNational Institute of TechnologyRourkelaIndia

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