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Related Experiment Video

Updated: May 26, 2026

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

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

Published on: August 26, 2016

Explainable gradient convolutional vector fuzzy pattern analysis based on ensemble model for facial expression

Lakshmi Sarvani Videla¹, Babu Reddy Mukamalla¹

¹Department of Computer Science, Krishna University, Machilipatnam, India.

Frontiers in Big Data

|May 25, 2026

Summary

This summary is machine-generated.

This study introduces a new method for facial expression recognition using machine learning and explainable AI (XAI). The novel approach achieves high accuracy and provides interpretable insights into emotion recognition.

Keywords:

ensemble machine learning algorithm explainable AI model facial expression recognition pattern recognition segmentation

Related Experiment Videos

Last Updated: May 26, 2026

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

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

Published on: August 26, 2016

Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Facial expression recognition (FER) utilizes machine learning (ML) to identify human emotions from facial features.
Explainable AI (XAI) is crucial for understanding the decision-making processes of ML models in FER, enhancing trust and fairness.
Current FER systems often lack transparency, limiting their reliability in real-world applications.

Purpose of the Study:

To propose a novel method for facial expression recognition (FER) by integrating ensemble machine learning with explainable AI (XAI).
To enhance the interpretability and trustworthiness of FER systems through a new segmentation technique.

Main Methods:

The proposed method involves preprocessing facial expression images for noise removal and normalization.
Segmentation is performed using a novel Explainable Gradient Convolutional Vector Fuzzy Pattern Recognition (ExGrConVFuzPR) model.
The ensemble ML and XAI approach was evaluated on benchmark datasets: JAFFE, CK, and AFLW.

Main Results:

The proposed method achieved high performance metrics: 97% accuracy, 96% precision, 96% recall, and 97% F1-score.
A low Root Mean Square Error (RMSE) of 0.043 indicates high prediction accuracy.
The results demonstrate the model's effectiveness in both recognizing facial expressions and providing interpretable results.

Conclusions:

The developed FER method offers a significant improvement in performance and interpretability.
The integration of ensemble ML and XAI provides a robust and trustworthy solution for emotion recognition.
This research contributes to the development of more transparent and reliable AI systems for understanding human emotions.