Muhammed Swavaf
Papers
2
Total Citations
41
H-Index
2
About
Muhammed Swavaf’s research lies at the intersection of computer vision and human-robot interaction, with a particular focus on how machines perceive and express emotion. In his highly cited work, “Navigating the YOLO Landscape: A Comparative Study of Object Detection Models for Emotion Recognition” (2024, 39 citations), Swavaf systematically evaluates state-of-the-art YOLO architectures for real-time emotion detection, addressing a critical gap in applying efficient object detectors to affective computing. This study provides a practical roadmap for deploying lightweight models in autonomous systems, robotics, and surveillance, where rapid emotional cue recognition is essential. His earlier work, “Human Perception of Emotional Responses to Changes in Auditory Attributes of Humanoid Agents” (2023), explores how vocal characteristics of robots influence human emotional reactions, bridging auditory design with social robotics. Though less cited, this foundational study underscores his commitment to creating more intuitive and emotionally resonant human-machine interfaces. Swavaf’s contributions are particularly valuable for researchers developing socially aware AI, as he combines rigorous benchmarking with human-centered design principles. His work continues to inform the next generation of emotionally intelligent autonomous agents.
Research Focus
Key Achievements
Top Papers
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