Topic:
Deep Learning for Still/Video-based Face Identification and Verification

Speaker: Dr. Jun-Cheng Chen (Research center of information technology innovation, Academia Sinica)

Date: 2020/1/3

Time: 14:20~16:20

Location: Delta building R216, NTHU

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.

Biography:
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.