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About

Adri Priadana is a researcher at the forefront of efficient computer vision, specializing in facial analysis and real-time object detection for human-robot interaction and embedded systems. His work focuses on developing lightweight, high-performance models that can operate on low-cost or CPU-based devices, bridging the gap between advanced AI and practical robotics. A key contribution is his work on gender recognition, where he proposed a novel architecture combining CNNs with a Bottleneck Transformer Encoder, achieving robust performance suitable for real-time applications like offline advertising and assistive robots. He also introduced Fast-PdNet, a fast person detector with an efficient multi-level contextual block, designed to support assistive robots in automatically interacting with users. In the domain of agriculture, Priadana has developed an efficient vision-based detector for automatic tomato classification, aiming to speed up production and ensure quality. His research extends to multi-view facial expression classification and facial attribute recognition for human-robot interaction, with his most-cited works (2022–2024) each garnering 2–4 citations. Through his focus on efficiency and edge deployment, Priadana is enabling the next generation of intelligent, responsive robots and automated systems.