[2020-12-25] Dr. Jun-Cheng Chen, Academia Sinica, "Deep Learning for Unconstrained Video-based Face Recognition"

Poster:Post date:2020-12-01
Title: Deep Learning for Unconstrained Video-based Face Recognition
Date: 2020-12-25 2:20pm-3:30pm
Location: R103, CSIE
Speaker: Dr. Jun-Cheng Chen, Academia Sinica
Hosted by: Prof. Shang-Tse Chen
Although deep learning approaches have achieved performance surpassing humans for still image-based face recognition, unconstrained video-based face recognition is still a challenging task due to large volume of data to be processed and intra/inter-video variations on pose, illumination, occlusion, scene, blur, video quality, etc. In this talk, we will talk about the challenging scenarios for unconstrained video-based face recognition from multiple-shot videos and surveillance videos with low-quality frames. Furthermore, we will present the required components for unconstrained video-based face recognition, including the modules for face/fiducial detection, face association, and face recognition. For example, the multi-scale single-shot face detectors can be used efficiently localize faces in videos. The detected faces are then grouped respectively through carefully designed face association methods, especially for multi-shot videos. Finally, the faces are recognized by the proposed face matcher based on an unsupervised subspace learning approach and a subspace-to-subspace similarity metric.
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.
Last modification time:2020-12-02 PM 2:08

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