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AdaBoost based Random forest model for Emotion classification of Facial images.

Kumari Gubbala1, M Naveen Kumar1, A Mary Sowjanya2

  • 1Department of CSE, CMR Engineering College, Hyderabad, Telangana, India.

Methodsx
|October 25, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel model for facial emotion analysis from social media images, achieving high accuracy. The developed AdaBoost based Random Forest classifier (ARFEC) offers improved performance over existing methods for reliable emotion detection.

Keywords:
2-dimensional discrete ortho-normal stock well transformation (DOST)AdaBoostAdaBoost and RandomForest ClassifierEmotion classificationFacial emotionRandom forestSentiment analysis

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Image Processing

Background:

  • Social media image sharing is prevalent, with users posting facial images depicting various emotions.
  • Accurate facial expression mining from these images is crucial for understanding user sentiment.
  • Existing emotion analysis models often lack sufficient accuracy and reliability for social media data.

Purpose of the Study:

  • To enhance the performance of emotion analysis in social media image posts.
  • To develop a novel model for accurate and reliable facial expression mining.
  • To improve the efficiency of emotion classification from visual data.

Main Methods:

  • Feature extraction using the 2D Ortho-normal Stockwell Transformation (DOST) method.
  • Feature selection implemented via bi-variate t-test.
  • Classification using an AdaBoost based Random Forest classifier for Emotion Classification (ARFEC).

Main Results:

  • The ARFEC model demonstrated high accuracy rates across multiple datasets: 89.5% on Flickr8k, 92.5% on CK+, and 89.5% on FER2013.
  • Comparative analysis showed ARFEC outperformed Support Vector Machine and K-Nearest Neighbors in overall accuracy.
  • The model effectively classifies six distinct emotional states: Sad, Fear, Awful, Happy, Surprised, and Satisfied.

Conclusions:

  • The proposed ARFEC model offers a significant improvement in facial emotion analysis accuracy and reliability.
  • Transformed features and the ARFEC approach are effective for mining emotions from social media images.
  • This research contributes a robust method for understanding human emotions expressed visually online.