Cognitive Resonance in Emotional Decoding: A CNN-ResNetB0 Synergy

Emotion recognition from facial expressions has gained significant attention for its abeyant applications in human-computer synergy and affective computing. In this research paper, we extant a human face emotion detection model planted on Convolutional Neural Networks (CNNs). Our model weights the power of CNNs to automatically learn and excerpt discriminative lineaments from facial portraits. We introduce a comprehensive dataset containing a diverse range of emotions, enabling robust model training and evaluation. Additionally, we explore the interpretability of the model by visualizing activation maps to identify the regions of the face contributing most to emotion classification. The proposed model's efficiency and realtime applicability make it a promising candidate for emotion-aware systems in real-world scenarios. This work offers a robust and interpretable solution for human face emotion detection, utilizing Convolutional Neural Networks, with high accuracy and potential applications in domains where understanding human emotions is crucial.