Bacha Rehman

Papers

Total Citations

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About

Bacha Rehman is a computer vision researcher whose work focuses on advancing real-time face detection and tracking systems—a critical challenge for applications like human-robot interaction and video analysis. His key research areas include hybrid deep learning models, convolutional neural networks, and template matching techniques for robust visual recognition. Rehman’s major contributions center on improving the accuracy and speed of face detection in dynamic environments. His most cited work, “Hybrid Model with Margin-Based Real-Time Face Detection and Tracking” (2017, 5 citations), introduces a novel approach that addresses the persistent trade-off between processing speed and detection reliability. He further refined this methodology in his 2018 paper (4 citations), which presents a margin-based region of interest (MROI) technique integrated with multi-task CNNs and template matching to enhance robustness in real-time setups. Though his citation counts are modest, Rehman’s work tackles a fundamental bottleneck in computer vision: achieving both high accuracy and low latency in uncontrolled settings. His research is particularly relevant for developers building responsive HRI systems and automated surveillance tools, offering practical solutions for real-world deployment challenges.