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Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation
07:12

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

Published on: August 26, 2016

Robust facial expression recognition via compressive sensing.

Shiqing Zhang1, Xiaoming Zhao, Bicheng Lei

  • 1School of Physics and Electronic Engineering, Taizhou University, Taizhou 318000, China. tzczsq@163.com

Sensors (Basel, Switzerland)
|June 28, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a novel sparse representation classifier (SRC) for robust facial expression recognition using compressive sensing (CS). The SRC method demonstrates superior performance and resilience against occlusions compared to traditional algorithms.

Keywords:
Gabor wavelets representationcompressive sensingcorruption and occlusionfacial expression recognitionlocal binary patternssparse representation

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Area of Science:

  • Computer Vision
  • Signal Processing
  • Pattern Recognition

Background:

  • Facial expression recognition is crucial for human-computer interaction.
  • Robustness to occlusions and corruptions remains a challenge in facial expression recognition.

Purpose of the Study:

  • To present a new method for robust facial expression recognition based on compressive sensing (CS) theory.
  • To evaluate the effectiveness and robustness of the proposed sparse representation classifier (SRC) on occluded facial images.

Main Methods:

  • Utilized compressive sensing (CS) theory to construct a sparse representation classifier (SRC).
  • Extracted three facial features: raw pixels, Gabor wavelets, and local binary patterns (LBP).
  • Evaluated SRC performance against nearest neighbor (NN), linear support vector machines (SVM), and nearest subspace (NS) on the Cohn-Kanade database.

Main Results:

  • The SRC method achieved better performance in facial expression recognition tasks.
  • SRC demonstrated stronger robustness to image corruption and occlusion compared to NN, SVM, and NS.
  • Experimental results validated the effectiveness of SRC on clean and occluded facial expression images.

Conclusions:

  • The proposed SRC method, based on CS theory, offers a robust solution for facial expression recognition.
  • SRC outperforms traditional methods, particularly in challenging conditions with occlusions.
  • This approach enhances the reliability of facial expression recognition systems.