Towards Intelligent Perception of Facial Images from End to End


Face is perhaps the most important visual object in computer vision, with extensive studies in the past decades. In the deep learning era, the performances of computer vision problems related to faces have been significantly boosted, many of which have already met the requirements in real-world applications. In the talk, I will first make some basic introductions on the face vision problems, including face detection, face alignment, face tracking, face attribute analyses and face recognition. Then I will introduce some of our previous works related to this topic. At last, I will point out some future trends in this direction. The main objective of this talk is to help the audiences get a comprehensive understanding of this relatively mature yet still hot research direction in computer vision.


Prof. Junliang XING
Institute of Automation, Chinese Academy of Sciences

Date & Time

14 Aug 2019 (Wednesday) 10:30 - 11:15


E11-G015 (University of Macau)

Organized by

Department of Computer and Information Science


Dr. Junliang XING received his dual B.E. degrees in Computer Science and Applied Mathematics from Xi'an Jiaotong University, 2007, and his Ph.D. degree in Computer Science and Technology from Tsinghua University, 2012. After that, he became an assistant professor within the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, where he is now a Professor and master student supervisor. Dr. Xing has published over 100 papers in peer-reviewed international conferences like ICCV, CVPR, ECCV, ACM Multimedia, AAAI, IJCAI, and journals like TPAMI, IJCV, TIP, PR. He has translated two books in computer vision and wrote one book on deep learning. Dr. Xing was the recipient of Google PhD Fellowship in 2011, the Best Paper Award of ACM International Conference on Multimedia in 2013, and the champions of many international AI technical competitions in face recognition, pose estimation, etc. His main research areas lie in pattern recognition, computer vision, and machine learning, with a main focus on vision problems related to human faces and bodies.