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Facial Expression Recognition Using Directional Gradient Local Ternary Patterns

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Multi-disciplinary Trends in Artificial Intelligence (MIWAI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11909))

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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.

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Authors and Affiliations

  1. College of Computer Science and Information Technology, Sudan University of Science and Technology, Khartoum, Sudan

    Nahla Nour

  2. School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa

    Serestina Viriri

Authors
  1. Nahla Nour
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  2. Serestina Viriri
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Correspondence to Serestina Viriri .

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Editors and Affiliations

  1. Mahasarakham University, Maha Sarakham, Thailand

    Rapeeporn Chamchong

  2. Murdoch University, Murdoch, WA, Australia

    Kok Wai Wong

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Nour, N., Viriri, S. (2019). Facial Expression Recognition Using Directional Gradient Local Ternary Patterns. In: Chamchong, R., Wong, K. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2019. Lecture Notes in Computer Science(), vol 11909. Springer, Cham. https://doi.org/10.1007/978-3-030-33709-4_8

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  • DOI: https://doi.org/10.1007/978-3-030-33709-4_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33708-7

  • Online ISBN: 978-3-030-33709-4

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