Refined Face Emotion Recognition using Convolutional Neural Networks with Adaptive Loss Functions for Real-Time Applications

This research study proposes a CNN-based method for face emotion recognition. The model is created using many convolutional layers, then pooling and dense layers, so effectively extracting and analyzing both linear and non-linear properties from facial images. The generalization and performance of the model are enhanced by means of data augmentation and adaptive loss functions including the fusion of softmax and center loss that combine High accuracy, precision, recall, and F1-scores are obtained by evaluating the proposed model on both real-time inputs and standard datasets.The results show the model is suited for practical usage since it can manage complicated, noisy, and blocked data. Underlining the efficiency of the CNN model in emotional recognition activities and assuming its uses in security, healthcare, and human-computer interaction, the research opens the path for further improvements in this field.