Salah Bouktif
Papers
1
Total Citations
39
H-Index
1
About
Salah Bouktif is a leading researcher in artificial intelligence and computer vision, with a particular focus on object detection, emotion recognition, and deep learning model optimization. His most impactful work, "Navigating the YOLO Landscape: A Comparative Study of Object Detection Models for Emotion Recognition" (2024, 39 citations), provides a comprehensive benchmark of YOLO architectures—from YOLOv5 to YOLOv8—for real-time facial expression analysis. This study bridges a critical gap in the literature by systematically evaluating trade-offs between detection speed and accuracy in emotion-aware systems, offering practical guidance for deploying lightweight models in autonomous vehicles, robotics, and surveillance. Bouktif's contributions extend to advancing efficient neural network design, enabling high-performance object detection on resource-constrained devices. His work has garnered significant attention, with citations reflecting its relevance to both academic research and industrial applications. By demystifying the YOLO ecosystem for emotion recognition, Bouktif has empowered practitioners to select optimal models for real-world deployment, solidifying his reputation as a key figure in applied computer vision and affective computing.
Research Focus
Key Achievements
Top Papers
- 1