Facial Expression Recognition Using Deep Learning (DL)

Facial Expression Recognition (FER) plays an important role in intelligent human–computer interaction systems by enabling machines to understand human emotions automatically. This paper presents a deep learning-based facial expression recognition system designed for real-time emotion detection with improved stability and accuracy. The objective of this work is to develop an efficient FER framework capable of recognizing human emotions under varying lighting and facial pose conditions. The proposed methodology uses convolutional neural networks (CNNs) for automatic feature extraction combined with emotion consistency filtering to reduce incorrect predictions caused by sudden facial variations. A preprocessing stage including face detection, normalization, and data augmentation improves robustness. The system also introduces emotion intensity estimation and privacy-aware processing where facial images are not stored after analysis. Experimental results demonstrate improved recognition accuracy and reduced prediction fluctuation compared with traditional machine learning approaches. The proposed system is suitable for real-time applications such as smart education, healthcare monitoring, and emotion-aware interactive systems.