Jun-Cheng Chen
Research Center for Information Technology Innovation, Academia Sinica
講者介紹：
Jun-Cheng Chen currently is an assistant research fellow at the research center for information technology innovation, Academia Sinica. He received his bachelor’s and master’s degrees in 2004 and 2006, respectively, both from Department of Computer Science and Information Engineering, National Taiwan University, Taipei. He received his Ph.D. degree from the University of Maryland, College Park, in 2016. He is a postdoctoral research fellow at the University of Maryland Institute for Advanced Computer Studies from 2017 to 2019. His current research interests include computer vision and machine learning with applications to face recognition and facial analysis. He was a recipient of the 2006 Association for Computing Machinery Multimedia Best Technical Full Paper Award.
演講摘要：
Recent developments in deep convolutional neural networks (DCNNs) have shown impressive performance improvements on various object detection/recognition problems (e.g., the performance of face recognition has achieved surpassing human performance on some standard challenging benchmarks). This has been made possible due to the availability of large annotated data and a better understanding of the nonlinear mapping between images and class labels, as well as the affordability of powerful graphics processing units (GPUs). These developments in deep learning have also improved the capabilities of machines in understanding faces and automatically executing the tasks of face detection, pose estimation, landmark localization, and face recognition from unconstrained images and videos. Besides these exciting advancements of technologies, it also raises a serious concern about the fairness, transparency, accountability, and security of these intelligent systems. In this talk, I will provide a brief overview of the development of deep-learning methods used for face recognition. I will also discuss about the potential concerns about the challenges and opportunities related to the model bias, model security, and data privacy.