Nisreen Khalil Abed

Papers

Total Citations

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About

Dr. Nisreen Khalil Abed is a leading researcher in the intersection of computer vision, machine learning, and human-computer interaction. Her most-cited work, "Gender Recognition of Human from Face Images Using Multi-Class Support Vector Machine (SVM) Classifiers" (2023), with 25 citations, addresses a critical challenge in robotics and security systems. Dr. Abed’s core contribution lies in developing robust, multi-class SVM frameworks that enhance the accuracy and reliability of gender classification from facial images—a problem essential for applications ranging from web search optimization to interactive robotics. Her research not only advances algorithmic performance but also provides practical solutions for real-world deployment in security and user-adaptive technologies. By refining feature extraction and classification techniques, she has helped bridge the gap between theoretical machine learning and applied biometric systems. Dr. Abed’s work is particularly notable for its focus on improving system responsiveness and fairness in automated recognition, making her a key figure in the ongoing evolution of intelligent, human-aware computing. Her findings continue to influence researchers developing next-generation interactive and surveillance technologies.