Abstract :

Facial emotion detection plays a vital role in human–computer interaction, surveillance, healthcare, and behavioral analysis. This project focuses on the detection and classification of human facial emotions using the Local Binary Pattern (LBP) technique. LBP is a powerful texture descriptor that captures local facial features efficiently. The proposed system processes facial images to extract texture patterns that represent different emotional expressions such as happiness, sadness, anger, fear, surprise, and neutrality. By converting facial regions into binary patterns, LBP provides robustness against illumination variations. The extracted features are then used for emotion classification using machine learning algorithms. The system aims to achieve high accuracy with low computational complexity. Experimental evaluation demonstrates that LBP-based facial emotion detection is effective for real-time applications. The approach is simple, fast, and suitable for implementation in res