Facial expression recognition (FER) is crucial for human-computer
interaction, emotion analysis, and psychological studies. Traditional
FER models face challenges in handling noisy video data caused by
lighting variations, occlusions, and facial distortions, which degrade
recognition accuracy. A noise-aware adaptive weighting model is
introduced to address these limitations. The proposed approach
leverages a spatiotemporal convolutional neural network (CNN)
combined with an attention-based adaptive weighting mechanism that
dynamically adjusts the contribution of frames based on their noise
level. The model processes video sequences by first extracting frame-
level features using a CNN. A noise detection module estimates the
noise level in each frame, and an adaptive weighting mechanism
assigns higher weights to less noisy frames. The weighted features are
then passed through a recurrent neural network (RNN) to capture
temporal dependencies. Experimental results on the CK+, Oulu-
CASIA, and AFEW datasets demonstrate that the proposed model
achieves 92.8% accuracy, outperforming existing FER methods such
as Temporal CNN and LSTM-based models. The adaptive noise-aware
weighting mechanism enhances robustness by reducing the impact of
noisy frames, leading to improved expression recognition.
V. Porkodi
Sivas University of Science and Technology, Turkey
Facial Expression Recognition, Adaptive Weighting, Noise-Aware, Spatiotemporal CNN, Recurrent Neural Network
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| Published By :
ICTACT
Published In :
ICTACT Journal on Data Science and Machine Learning
( Volume: 6 , Issue: 2 , Pages: 751 - 755 )
Date of Publication :
March 2025
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