Wonjun Hwang's Personal Site

Wonjun Hwang (황원준), Ph. D. 

Associate Professor, 
Dept. of Software and Computer Engineering and Dept. of Artificial Intelligence, 
College of Information Technology,
Ajou University (아주대학교)

Email: wjhwang_at_ajou.ac.kr

Tel: +82-31-219-2632
Fax: +82-31-219-1621
Office Address: 703 Paldal hall, San 5-1, Woncheon-dong, Yeongtong-gu, Suwon-si, Gyeonggi-do, 16499, Korea

Wonjun Hwang received both B.S. and M.S. degrees from the Department of Electronics Engineering, Korea University, Korea, in 1999 and 2001, respectively, and Ph.D. degree in the School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Korea, in 2016. From 2001 to 2008, he was a research staff member in Samsung Advanced Institute of Technology (SAIT), Korea. In 2004, he contributed to the promotion of Advanced Face Descriptor, Samsung and NEC joint proposal, to MPEG-7 international standardization. In 2006, he proposed the SAIT face recognition method which achieved the best accuracy under the uncontrolled illumination situation at Face Recognition Grand Challenge (FRGC) and Face Recognition Vendor Test (FRVT). In 2006, he developed the real-time face recognition engine for the Samsung cellular phone, SGH-V920. From 2009 to 2011, he was a senior engineer in Samsung Electronics, Korea, where he worked on developing face and gesture recognition methods for Samsung humanoid robot, a.k.a RoboRay. In 2011, he rejoined the SAIT as a research staff member and from 2011 to 2014 he worked for a 3D medical image processing of Samsung surgical robot. From 2014 to 2016, he worked on developing deep learning-based face detection and recognition methods for Samsung Galaxy series. In 2016, he joined the department of Software and Computer Engineering, Ajou University, Korea and is now an associate professor. His research interests are in computer vision, pattern recognition, and deep learning.

If you wanna visit my lab website, plz, click the following URL

  • Deep Learning
  • Pattern Recognition
  • Computer Vision
  • Face Recognition