Massimo Tistarelli
意大利萨萨里大学终身教授,IAPR Fellow
简介:
Massimo Tistarelli is currently a Tenured Full Professor of Computer Science and the Director of the Computer Vision Laboratory, University of Sassari, Italy. He received the Ph.D. degree in computer science and robotics from the University of Genoa, Italy, in 1991. His main research interests cover biological and artificial vision, pattern recognition, biometrics, visual sensors, robotic navigation, and visuomotor coordination. He is one of the world-recognized leading researchers in biometrics and has directed the Summer School on Biometrics for the past 20 years. He is AE for IVC, IET Biometrics and PR and was AE for TPAMI and PRL. He served as chair of the IEEE Italian Chapter of the Biometrics Council for two terms, the IAPR Technical Committee on Biometrics and the First Vice President of the IAPR (2014-2018). He has served as Vice-President Technical Activities of the IEEE Biometrics Council (2019-2021), chair of the IAPR Fellow Committee (2018-), member of the IEEE Distinguished Lecturers Program. He has also served as member of the IEEE Biometrics Certification Program. He is a fellow of the IAPR, Senior Member of the IEEE.
报告题目:Face Recognition: a Vision Ahead Reflections on 30 years of face recognition research
报告摘要:Face recognition is possibly one of the most successful applications of Computer Vision and AI. Today's information technology allowed to deploy face recognition in several domains, ranging from automated border control to mobile device authentication. Even though the progress in computing power and machine learning allowed to implement very fast and efficient systems, there are still several issues which remain unsolved. On the other hand, the basic "face recognition pipeline", conceived 30 years ago, still remains unaltered. As such, we need to learn from the past and address some research questions which are still unanswered. Among them:
1. If face recognition is a "solved" problem, why are we still doing research on this topic?
2. What are the drawbacks and limitations of current deep learning models? How far can we go by exploiting increasing amounts of face data?
3. Is the human visual system still the best comparative face recognition model? If so, what can we learn from the way humans recognize faces?
4. How can we build "ethical" systems which properly address current privacy concerns?
In this talk we'll address these questions, trying to envisage a path forward with the aim of driving our research curiosity towards the design of tomorrow's intelligent machines.