Facial Expression Recognition Using Entire Gabor Filter Matching Score Level Fusion Approach Based on Subspace Methods

  • Ganapatikrishna Hegde
  • M. Seetha
  • Nagaratna Hegde
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9468)


In this study appearance based facial expression recognition is presented by extracting the Gabor magnitude feature vectors (GMFV) and Gabor Phase Congruency vectors (GPCV). Feature vector space of these two vectors dimensions are reduced and redundant information is removed using subspace methods. Both GMFV and GPCV spaces are projected with Eigen score and projected matching scores are normalized and fused. Final matching score of each subspace method are normalized using Z-score normalization and fused together using maximum rule. Dimension of entire Gabor feature vector space consumes larger area of memory and high processing time with more redundant data. To overcome this problem in this paper entire Gabor matching score level fusion (EGMSLF) approach based on subspace methods is introduced. The JAFFE database is used for experiment. Support vector machine classifier technique is used as classifier. Performance evaluation is carried out by comparing proposed approach with state of art approaches. Proposed EGMSLF approach enhances the performance of earlier methods.


Gabor filter Expression recognition Computation time Subspace Dimension reduction Phase congruency 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ganapatikrishna Hegde
    • 1
    • 2
  • M. Seetha
    • 3
    • 4
  • Nagaratna Hegde
    • 5
  1. 1.Department of Computer ScienceSDMITUjireIndia
  2. 2.VTUBelgaumIndia
  3. 3.Department of Computer ScienceGNITSHyderabadIndia
  4. 4.JNTUHyderabadIndia
  5. 5.Department of Computer ScienceVCEHyderabadIndia

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