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Local Dominant Directional Symmetrical Coding Patterns for Facial Expression Recognition.

Ying Tong1, Rui Chen1

  • 1College of Communication Engineering, Nanjing Institute of Technology, Nanjing 211167, China.

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|June 21, 2019
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Summary
This summary is machine-generated.

We introduce Local Dominant Directional Symmetrical Coding Patterns (LDDSCPs), a new method for facial expression recognition. LDDSCP offers improved accuracy and efficiency compared to existing techniques like Local Binary Patterns (LBPs).

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Pattern Recognition

Background:

  • Local Binary Patterns (LBPs) suffer from inaccurate textural direction representation and high computational complexity.
  • Existing facial expression recognition methods often struggle with precise feature description and efficiency.

Purpose of the Study:

  • To propose a novel feature descriptor, Local Dominant Directional Symmetrical Coding Patterns (LDDSCPs), to address the limitations of LBPs.
  • To enhance facial expression recognition accuracy and reduce computational complexity.

Main Methods:

  • Inspired by the human visual system, LDDSCP partitions convolution masks into symmetrical groups based on direction.
  • It computes convolution values and encodes dominant direction information by comparing pixel values to group averages, generating LDDSCP-1 and LDDSCP-2 codes.
  • Corresponding histograms of these codes are stacked to form the LDDSCP feature vector.

Main Results:

  • LDDSCP demonstrates superior performance in recognition rate and computational complexity compared to LBP and Gabor operators on JAFFE and Cohn-Kanade databases.
  • The proposed descriptor performs comparably to state-of-the-art methods like LDP, LDNP, es-LBP, and GDP.

Conclusions:

  • LDDSCP provides a more precise facial feature description with lower computational complexity.
  • The novel descriptor offers a promising advancement for facial expression recognition systems.