Dictionary Based Approach for Facial Expression Recognition from Static Images

  • Krishan SharmaEmail author
  • Renu Rameshan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10481)


We present a simple approach for facial expression recognition from images using the principle of sparse representation using a learned dictionary. Visual appearance based feature descriptors like histogram of oriented gradients (HOG), local binary patterns (LBP) and eigenfaces are used. We use Fisher discrimination dictionary which has discrimination capability in addition to being reconstructive. The classification is based on the fact that each expression class with in the dictionary spans a subspace and these subspaces have non-overlapping directions so that they are widely separated. Each test feature point has a sparse representation in the union of subspaces of dictionary formed by labeled training points. To check recognition performance of the proposed approach, extensive experimentation is done over Jaffee and CK databases. Results show that the proposed approach has better classification accuracy than state-of-the-art techniques.


Histogram of oriented gradient Local binary pattern Eigenfaces Dictionary learning Sparse representation Facial expression recognition 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Computing and Electrical EngineeringIndian Institute of TechnologyMandiIndia

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