Chun-Ting Huang received his B.S. degree in electrical engineering, from National Tsing Hua University, Hsinchu, Taiwan in 2008. He received his M.S. and Ph.D. degrees in Electrical Engineering with an emphasis in Multimedia Processing and Computer Vision from University of Southern California, Los Angeles in May 2011 and Janurary 2017, respectively. He worked as a research assistant in Media Communications Lab during his pursuing of Doctorate degree. Chun-Ting’s dissertation title is "Facial Identity Recognition and Attribute Classification Using Machine Learning Techniques". His research interests include computer vision, image processing, pattern recognition, and machine learning. After graduated in 2017, Chun-Ting is now working as a researcher in Qualcomm, San Diego.
"Facial Recognition" has become an important technique to handle the tremendous growing need for identification and verification since last century. The replacement of traditional transaction by electronic transaction successfully gathered attention for facial recognition from research and business communities, because facial recognition requires no physical interaction on behalf of users. The research on facial recognition can be traced back to early 1990s, from the Eigenface proposed by Turk and Pentland in 1991, which has over 11409 citations on Google Scholar. The follow-up development can be concluded into general directions discussed in Face Recognition Vendor Test - FRVT 2002, and different face databases are developed in order to solve various conditions, such as poses, expressions, and environment. A new database called Long Distance Heterogeneous Face Database (LDHF-DB) is focused on face images under various distances and near-infrared camera, which provides an new challenge within this field.
Since under the long distance, the near-infrared camera can only capture blurred and vague face images, causing the template feature’s low performance on LDHF-DB. Therefore, our research only adopts geometric and shape-based features, locally and globally, to determine the input of structured-fusion method. Based on the different characteristics of features we collected from database, we aim to develop a robust classification algorithm with machine learning to distinguish faces under various qualities.
Huang, C.-T.; Chen, Y.; Lin, R. & Kuo, C.-C. Age/Gender Classification with Whole-Component Convolutional Neural Networks (WC-CNN). APSIPA ASC, 2017 PDF
Huang, C.-T.; Wang, Z. & Kuo, C.-C. Visible-Light and Near-Infrared Face Recognition at A Distance. Journal of Visual Communication and Image Representation, 2016 PDF
Huang, C.-T.; Wang, Z. & Kuo, C.-C. TAEF: A Cross-Distance/Environment Face Recognition Method Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2015, 1-8 PDF
Huang, C.-T.; Huang, L.; Qin, Z.; Yuan, H.; Zhou, L.; Varadharajan, V. & Kuo, C.-C. Survey on Securing Data Storage in the Cloud. APSIPA Transactions on Signal and Information Processing, Cambridge Univ Press, 2014, 3, e7 DOI
Huang, C.-T. Improving the Multimedia Processing of Relay Nodes in Mesh Wireless Networks. Intelligent Information Hiding and Multimedia Signal Processing, 2010, 260-263 DOI
Huang, C.-T. The Study of Balance Traffic Load with Genetic Algorithm for PON. Intelligent Information Hiding and Multimedia Signal Processing, 2008, 39-42 DOI
Tseng, P.-C.; Shiung, J.-K.; Huang, C.-T.; Guo, S.-M. & Hwang, W.-S. Adaptive Car Plate Recognition in QoS-aware Security Network. Second International Conference on Secure System Integration and Reliability Improvement, 2008, 120-127 DOI
Chun-Ting Huang, phD
University of Southern California
EEB 433, 3740 McClintock Ave
Los Angeles, CA 90089-2564
cthuang.a (at) gmail (dot) com
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