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Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
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Luminance sticker based facial expression recognition using discrete wavelet transform for physically disabled

R Nagarajan1, M Hariharan, M Satiyan

  • 1University Malaysia Perlis, Kangar, Perlis, Malaysia.

Journal of Medical Systems
|April 6, 2011
PubMed
Summary

This study introduces a novel method for facial expression recognition using luminance stickers and Discrete Wavelet Transform (DWT). The approach shows promising accuracy for assisting immobilized individuals through facial cues.

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Area of Science:

  • Biomedical Engineering
  • Computer Vision
  • Human-Computer Interaction

Background:

  • Facial expression recognition is crucial for assistive technologies for disabled and immobilized individuals.
  • Developing robust and accurate systems remains a significant research challenge.

Purpose of the Study:

  • To propose and evaluate a luminance sticker-based facial expression recognition system.
  • To investigate the effectiveness of Discrete Wavelet Transform (DWT) for feature extraction in this context.
  • To compare the performance of various wavelet families and classifiers for accuracy and computational efficiency.

Main Methods:

  • Facial expressions captured using luminance stickers.
  • Feature extraction performed using Discrete Wavelet Transform (DWT) with multiple wavelet families (db, Coif, Sym) and orders.

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  • Feature vectors generated from the standard deviation of first-level wavelet coefficients.
  • Classification using Artificial Neural Network (ANN), k-Nearest Neighborhood (kNN), and Linear Discriminant Analysis (LDA).
  • Validation using conventional and cross-validation techniques.

    • Main Results:

    • The proposed luminance sticker-based facial expression recognition method achieved promising classification accuracies.
    • Different wavelet families and orders demonstrated varying performance and computational times.
    • The feature vectors derived from DWT coefficients proved effective for classification.

    Conclusions:

    • The DWT-based feature extraction for luminance sticker facial expressions is a viable approach for assistive technology.
    • The study provides insights into optimal wavelet choices for facial expression recognition.
    • The system shows potential for enhancing human-computer interaction for individuals with physical disabilities.