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Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm

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Enhancing feature selection for multi-pose facial expression recognition using a hybrid of quantum inspired firefly

Mu Panliang¹, Sanjay Madaan², Siddiq Ahmed Babikir Ali³

¹National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University, Chengdu, 610017, China.

Scientific Reports

|February 7, 2025

Summary

A new hybrid algorithm, QIFABC, enhances facial expression recognition (FER) accuracy by improving feature selection for multi-pose analysis. This method boosts the performance of deep learning models like ResNet-50 in recognizing emotions across different head angles.

Keywords:

Artificial Bee colony Deep learning Facial expression recognition Feature selection Firefly Algorithm Metaheuristic Multi-pose expressions Quantum Computing

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Facial Expression Recognition (FER) is crucial for AI applications like healthcare and human-computer interaction.
Accurate FER relies heavily on effective feature selection, especially for diverse poses.
Existing methods often struggle with the robustness required for multi-pose FER systems.

Purpose of the Study:

To propose a novel hybrid algorithm, QIFABC (Hybrid Quantum-Inspired Firefly and Artificial Bee Colony Algorithm), for enhanced feature selection in multi-pose FER.
To improve the accuracy and robustness of facial expression recognition systems across various head poses.
To evaluate the efficacy of QIFABC against individual algorithms and assess its performance with deep neural networks.

Main Methods:

Developed the QIFABC algorithm, integrating Quantum-Inspired Firefly Algorithm (QIFA) for initial search and Artificial Bee Colony (ABC) for refined search.
Employed QIFABC for feature selection, comparing it with QIFA, Firefly Algorithm (FA), and ABC.
Utilized the selected features to train and evaluate the ResNet-50 deep neural network model for facial expression classification.

Main Results:

The QIFABC algorithm demonstrated superior feature selection capabilities compared to QIFA, FA, and ABC.
FER systems using QIFABC with ResNet-50 achieved high accuracy across multiple poses on RaF and KDEF datasets.
Specific accuracies reached up to 98.93% (front pose, RaF dataset) and 98.47% (front pose, KDEF dataset).

Conclusions:

The proposed QIFABC algorithm significantly enhances feature selection for multi-pose facial expression recognition.
QIFABC offers improved robustness and accuracy, outperforming individual algorithms in FER tasks.
The hybrid approach provides a promising direction for developing more effective FER systems integrated with deep learning.