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Facial Expression Recognition by Fusing Gabor and Local Binary Pattern Features

  • Yuechuan Sun
  • Jun YuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10133)

Abstract

Obtaining effective and discriminative facial appearance descriptors is a challenging task for facial expression recognition (FER). In this paper, a new FER method which combines two of the most successful facial appearance descriptors, namely Gabor filters and Local Binary Patterns (LBPs), is proposed considering that the former one can represent facial shape and appearance over a broader range of scales and orientations while the latter one can capture subtle appearance details. Firstly, feature vectors of Gabor and LBP representations are generated from the preprocessed face images respectively. Secondly, feature fusion is applied to combine these two vectors and dimensionality reduction is conducted. Finally, the Support Vector Machine (SVM) is adopted to classify prototypical facial expressions using still images. The experimental results on the CK+ database demonstrate that the proposed method promotes the performance compared with that using Gabor or LBP descriptor alone, and outperforms several other methods.

Keywords

Facial expression recognition Gabor wavelet Local binary patterns Feature fusion 

Notes

Acknowledgement

This work is supported by the National Natural Science Foundation of China (No. 61572450 and No. 61303150), the Open Project Program of the State Key Lab of CAD&CG, Zhejiang University (No. A1501), the Fundamental Research Funds for the Central Universities (WK2350000002), the Open Funding Project of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (No. BUAA-VR-16KF-12).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of AutomationUniversity of Science and Technology of ChinaHefeiChina
  2. 2.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingPeople’s Republic of China

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