Noor Almaadeed

Papers

Total Citations

H-Index

About

Noor Almaadeed is a leading researcher in the fields of computer vision, affective computing, and pattern recognition, with a particular focus on facial expression analysis and emotion recognition. Her most-cited work, "Emotion recognition from scrambled facial images via many graph embedding" (2017, 62 citations), introduces an innovative approach that leverages graph-based embedding techniques to accurately identify emotions even from partially obscured or scrambled facial images—a significant challenge in real-world surveillance and human-computer interaction. This contribution has advanced the robustness of emotion recognition systems, enabling more reliable performance under adverse conditions. Beyond this, Almaadeed's research has explored deep learning architectures and feature extraction methods for biometrics and video analysis, consistently pushing the boundaries of how machines interpret human affective states. Her work is widely cited and has influenced subsequent studies in both academic and applied settings, including security, healthcare, and assistive technologies. Through her dedication to solving complex visual recognition problems, Noor Almaadeed has established herself as a key figure in making emotion-aware systems more practical and resilient.