Institute of Information Science Academia Sinica
Topic: TIGP (SNHCC) – Deep Learning for Understanding Faces
Speaker: Prof. Jun-Cheng Chen (Research Center for Information Technology Innovation, Academia Sinica)
Date: 2019-09-11 (Wed) 14:00 – 16:00
Location: Auditorium 108 at IIS Old Building
Host: TIGP SNHCC Program

Abstract:

    Recent developments in deep convolutional neural networks (DCNNs) have shown impressive performance improvements on various object detection/recognition problems. 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. In this talk, I will provide an overview of deep-learning methods used for face recognition. I will discuss different modules involved in designing an automatic face recognition system and the role of deep learning for each of them. Some open issues regarding DCNNs for face recognition problems are then discussed.


BIO:

   Jun-Cheng Chen currently is an assistant research fellow at the research center of 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.