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About

Son Lam Phung is a researcher whose work bridges the critical domains of affective computing and agricultural robotics. His early and most impactful contributions center on facial expression recognition, where he developed novel methods for feature selection and classification, including the use of trainable 2-D filters combined with Support Vector Machines. His seminal 2010 paper on feature selection for facial expression recognition has garnered 22 citations, establishing a foundation for automatic emotion detection in human-computer interaction. Phung’s work specifically advanced the accurate recognition of key expressions like smiling and neutrality, enabling more perceptive and responsive robotic and gaming interfaces. More recently, Phung has applied his computer vision expertise to the pressing challenges of agricultural robotics. He has addressed the robustness of fruit detection and picking-point localisation under real-world environmental disturbances, proposing multi-vision-based strategies that improve grasping accuracy and reduce economic loss from fruit damage. His 2025 publications on these topics, including work with the YOLOv8 deep learning model, signal a significant pivot toward practical, field-deployable systems. Additionally, his exploration of transformer-based networks for point cloud completion (CenFormer) demonstrates a commitment to solving fundamental 3D perception problems that underpin autonomous navigation and augmented reality. Phung’s career reflects a versatile and impactful trajectory from human emotion analysis to intelligent, sensor-driven automation.