Facial expression recognition (FER) plays a critical role in human-
computer interaction, enabling systems to interpret and respond to
human emotions effectively. Traditional FER methods struggle to
handle variations in viewpoint, illumination, and facial occlusions,
leading to reduced accuracy and robustness. A neural network-based
ensemble approach is proposed to address these challenges by
combining the strengths of multiple deep learning models to enhance
multi-view facial expression recognition. The proposed method
integrates Convolutional Neural Networks (CNNs) with a stacking
ensemble mechanism, where individual CNN models are trained on
different facial angles and lighting conditions. The outputs of these
models are combined using a meta-classifier to improve recognition
accuracy. The ensemble network is trained and tested on the Multi-PIE
and BU-3DFE datasets, achieving an accuracy of 96.4%,
outperforming existing single-model approaches. The proposed
method demonstrates robustness across varying facial poses and
occlusions, highlighting its potential for real-world applications in
emotion-aware systems and interactive technologies.
Karthikeyan Thangavel, P. Sowmya Saraswathi, Saravanan Velusamy
University of Technology and Applied Sciences, The Sultanate of Oman
Facial Expression Recognition, Multi-View, Ensemble Learning, CNN, Meta-Classifier
| January | February | March | April | May | June | July | August | September | October | November | December |
| 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Published By :
ICTACT
Published In :
ICTACT Journal on Data Science and Machine Learning
( Volume: 6 , Issue: 2 , Pages: 747 - 750 )
Date of Publication :
March 2025
Page Views :
17
Full Text Views :
3
|