Abstract:

Face recognition and emotion detection systems have become essential components in modern computer vision applications, enabling machines to interpret human identity and emotional states from facial images. This work presents an integrated approach that combines facial recognition with emotion classification using deep learning techniques. The system employs a Convolutional Neural Network to automatically extract features and identify facial expressions such as happiness, sadness, anger, fear, and surprise. Preprocessing techniques such as normalization and image resizing are applied to improve model performance. The proposed method is trained and evaluated on standard facial expression datasets, demonstrating improved accuracy compared to traditional feature-based approaches. The system shows potential for real-time applications in areas such as surveillance, healthcare monitoring, and human–computer interaction. Despite its effectiveness, challenges related to varying lighting conditions, occlusion, and dataset bias remain important considerations for future work.

Description:

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