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Facial Expression Recognition Using Directional Gradient Local Ternary Patterns

  • Nahla Nour
  • Serestina ViririEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11909)

Abstract

Extraction of human emotions from facial expression has attracted significant attention in computer vision community. There are several appearance based techniques like local binary patterns (LBP), local directional patterns (LDP), local ternary patterns (LTP) and gradient local ternary patterns (GLTP). Recently, many investigations have been done to improve these feature extraction techniques. Although GLTP has achieved an improvement in robustness to noise and illumination, it encodes image gradient in four directions and two orientations only. This paper proposes to improve GLTP to directional gradient local ternary patterns (DGLTP) by encoding image gradient on eight directions and four orientations. The eight directional Kirsch mask is used to encode the image gradient followed by dimensionality reduction using linear discriminant analysis (LDA) and AVG, MAX and MIN pooling techniques are compared for fusing facial expression features. The proposed technique was experimented on JAFFE facial expression dataset with support vector machine (SVM). The experimental results show that proposed technique improved accuracy of GLTP.

Keywords

Local binary pattern Local directional pattern Local ternary pattern Gradient local ternary pattern Directional gradient local ternary pattern 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.College of Computer Science and Information TechnologySudan University of Science and TechnologyKhartoumSudan
  2. 2.School of Mathematics, Statistics and Computer ScienceUniversity of KwaZulu-NatalDurbanSouth Africa

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