ADAPTIVE NOISE-AWARE VIDEO-BASED WEIGHTING FOR ENHANCED FACIAL EXPRESSION RECOGNITION

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.