Emotion Recognition from Facial Expressions Using CNNs
DOI:
https://doi.org/0.15662/IJEETR.2026.0801013Keywords:
Convolutional Neural Networking(CNN), Facial Expression Recognition(FER), Emotion Classification, Image Analysis, Basic Emotion, Human Computer InteractionAbstract
The emotion recognition from facial expressions is essential for applications in human–computer interaction, affective computing, and intelligent surveillance Convolutional Neural Networks (CNNs) automatically learn discriminative facial features, removing the need for manual feature extraction. The proposed CNN framework classifies emotions such as happiness, sadness, anger, fear, surprise, disgust, and neutrality using static facial images. Training incorporates benchmark datasets, data augmentation, and regularization techniques to improve model generalization and reduce overfitting. Experimental results show high classification accuracy, demonstrating the effectiveness of CNNs for real-time emotion recognition in domains like healthcare, education, entertainment, and security. Moreover, manual feature extraction limits scalability and adaptability across diverse datasets and real-time applications. The proposed system employs CNN-based deep learning architectures for automatic feature extraction and emotion classification. The proposed CNN system achieves an accuracy of approximately 72%, demonstrating strong capability in capturing subtle facial variations and reliably identifying emotional states.
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