Institute of Automation Chinese Academy of Sciences (CASIA), China
Iris Recognition: Progress and Challenges
King-Sun Fu Prize Lecture
Abstract: Iris recognition has proven to be a most reliable biometric solution for personal identification and has received much attention from the pattern recognition community. However, it is far from being a solved problem as many open issues remain to be resolved to make iris recognition more user-friendly and robust. In this talk, I will present an overview of our decades’ efforts on iris recognition, including iris image acquisition, iris image pre-processing, iris feature extraction and security issues of iris recognition systems. I will discuss our most recent work on light-field iris recognition and all-in-focus simultaneous iris recognition of multiple people at a distance. Examples will be given to demonstrate the successful routine use of our work in a wide range of fields such as mobile payment, banking, access control, welfare distribution, etc. I will also address some of the remaining challenges as well as promising future research directions before closing the talk.
Prof. Jiliang Tang
MSU Foundation Professor, Data Science and Engineering Lab, Michigan State University, USA
Graph Neural Networks: Models, Trustworthiness, and Applications
J. K. Aggarwal Prize Lecture
Abstract: Graph Neural Networks (GNNs) have shown their power in graph representation learning. They have advanced numerous recognition and learning tasks in many domains such as biology and healthcare. In this talk, I will first introduce a novel perspective to understand and unify existing GNNs that paves a principled and innovative way to design new GNN models. As GNNs become more pervasive, there is an ever-growing concern over how GNNs can be trusted. Then I will discuss how to build trustworthy GNNs. Given that graphs have been leveraged to denote data in real-world systems, I will finally demonstrate representative applications of GNNs.
Prof. Yunhong Wang
School of Computer Science and Engineering, Beihang University, China
Towards Practical Biometrics: Face and Gait
Maria Petrou Prize Lecture
Abstract: Biometrics are unique physical or behavioural characteristics that can be adopted for identification. In the last few years, substantial advancements have been made in this field with the development of deep learning theories and technologies. This is evidenced by not only the high results on large-scale benchmarks but also the attempts accounting for soft-biometrics, including gender, expression, age, etc. Meanwhile, recent studies show additional challenges in uncontrolled conditions, such as severe variations in scale, pose, illumination, occlusion and cluttered background, which should be well handled for real-world applications. This talk focuses on two typical representatives, face recogniiton and gait recognition, with dedicatedly designed deep learning based methodologies towards practical use, covering the tasks from identity recognition to attribute analysis, presenting the latest progress on the interpretability and robustness of deep neural networks. Finally, some perspectives are discussed to facilitate future research.