Papers
4
Total Citations
36
H-Index
4
About
Paola Barra’s research sits at the intersection of human-robot interaction, computer vision, and intelligent systems for smart environments. Her work focuses on making robots more context-aware and secure, particularly through trust models that defend humanoid robots like Pepper against cyberattacks in smart ecosystems. She has also advanced emotion recognition with a novel “web-shaped model” that improves human-robot and human-computer interaction, and developed faster head pose estimation methods using gradient boosting and Partitioned Iterated Function Systems—critical for applications in robotics, biometrics, and surveillance. Her most cited paper (14 citations) addresses contextual trust in humanoid robots, while her emotion recognition work (11 citations) and head pose estimation research (7 citations) demonstrate her impact on practical, real-time AI systems. Notably, Barra has also explored digital twin construction for marine drones using AWS RoboMaker, showcasing her versatility in applying cloud and robotic simulation technologies to autonomous systems. Her contributions are shaping more secure, perceptive, and efficient robotic platforms for both terrestrial and marine environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2Emotion recognition by web-shaped model11 citations · 2022
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