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About

Vincenzo Vigilante is a leading researcher at the intersection of computer vision, affective computing, and social robotics. His work focuses on enabling machines to perceive and interpret human attributes—specifically age, gender, and emotion—from facial images, with the goal of creating more intuitive and responsive robotic systems. Vigilante’s most impactful contribution is his pioneering use of knowledge distillation to train convolutional neural networks for age estimation, a method detailed in his 2021 paper (35 citations) that significantly improves efficiency without sacrificing accuracy. He has also advanced facial emotion recognition in unconstrained, real-world settings (31 citations, 2022), moving beyond controlled benchmarks to tackle the challenges of “in the wild” applications. His practical systems for gender recognition on mobile robots (21 citations, 2019) and emotion analysis for social robotics (18 citations, 2019) demonstrate a clear trajectory from algorithmic innovation to deployment. A notable achievement is the development of MIVIABot, a cognitive robot designed for smart museum interactions (12 citations, 2019), which showcases his ability to integrate perception, reasoning, and human-robot interaction into a cohesive, real-world platform. With over 100 total citations, Vigilante’s work is shaping the future of socially aware robotics.