新鲜出炉 | CCBR2019公布特邀演讲嘉宾

中国生物特征识别大会（Chinese Conference on Biometric Recognition，简称CCBR）是由中国人工智能学会（CAAI）主办的国内生物特征识别领域的学术盛会，第十四届中国生物特征识别大会（CCBR2019）将于2019年10月12~13日在湖南株洲举行。应CCBR2019大会组委会的邀请，美国密歇根州立大学Anil K. Jain教授，美国南加州大学C.-C. Jay Kuo教授，中国科学院自动化所谭铁牛研究员将于10月12日在大会上作主题演讲。

演讲嘉宾介绍

1

Anil K. Jain

密歇根州立大学杰出教授

美国国家工程院院士、TPAMI主编、IEEE/ACM Fellow

演讲题目：Presentation Attacks: Detecting Fingerprint, Face and Iris Spoofs

2

C.-C. Jay Kuo

美国南加州大学杰出教授

IEEE/SPIE/AAAS Fellow

演讲题目：Towards Effective and Explainable Biometrics

3

谭铁牛

中国科学院自动化所研究员

中国科学院院士、IEEE/IAPR Fellow

演讲题目：Recent Advances and Future Directions of Biometric Recognition

生物识别是模式识别、图像处理、人工智能等学科领域的前沿方向，其在安全领域发挥着重要作用。自2000年以来，CCBR已经在北京、杭州、西安、广州、济南、沈阳、天津、成都、深圳和乌鲁木齐等地成功举办了13届，有力地促进了国内本领域的学术和技术发展。

CCBR2019希望能够促进学术与产业的交流，促进生物特征识别在各个行业中的深度应用。现向广大科技工作者公开征集优秀学术论文（英文），大会录用的稿件将由SPRINGER出版社的LECTURE NOTES IN COMPUTER SCIENCE（LNCS）图书系列出版，并被EI和ISTP检索。欢迎大家踊跃投稿和参加CCBR2019！

CCBR2019官网网站：

http://www.ccbr99.cn/

CCBR2019投稿模板：http://www.ccbr99.cn/contributions.jsp

CCBR2019在线投稿系统：https://easychair.org/conferences/?conf=ccbr2019

Speaker

Anil K. Jain

密歇根州立大学杰出教授

美国国家工程院院士、TPAMI主编、IEEE/ACM Fellow

Title

Presentation Attacks: Detecting Fingerprint, Face and Iris Spoofs

Abstract

The primary purpose of a biometric recognition system is to ensure a reliable and accurate user authentication, but the security of the recognition system itself is subject to compromise by presentation attacks. The ISO standard IEC 30107-1:2016(E) defines presentation attacks as the “Presentation to the biometric data capture subsystem with the goal of interfering with the operation of the biometric system”. One of the most common ways to realize presentation attacks is using biometric spoofs. With widespread deployment of biometric systems for unlocking smartphones, access control, mobile payments, international border security, and national registration systems, there is an urgent need to detect and prevent spoof attacks. This talk will present our ongoing research in developing accurate, generalizable (previously unseen spoof types), and efficient hardware and software solutions for detecting fingerprint, face, and iris spoof attacks that will achieve 98% TDR @ 0.2% FDR. 

Website: http://biometrics.cse.msu.edu/

Biography

Anil K. Jain is a University Distinguished Professor at Michigan State University.  His research expertise is in pattern recognition, computer vision, and biometrics. Jain is a Fellow of IEEE and ACM and was the Editor-in-Chief of the IEEE Trans. on Pattern Analysis & Machine Intelligence. He received Guggenheim, Humboldt, Fulbright, and King Sun Fu awards. Jain is a member of the United States National Academy of Engineering, Indian National Academy of Engineering and The World Academy of Sciences. His publications are at https://tinyurl.com/jainscholar

Speaker

C.-C. Jay Kuo

美国南加州大学杰出教授

IEEE/SPIE/AAAS Fellow

Title

Towards Effective and Explainable Biometrics

Abstract

Deep learning provides state-of-the-art biometrics solutions when training and testing data share similar distributions and the number of training samples is sufficiently larger. The deep-learning-based solutions are mathematically intractable due to the non-convex optimization nature. Furthermore, their robustness is a main concern. To search for effective and explainable biometrics solutions is challenging yet essential. In this talk, I will present a path towards this direction and provide preliminary results using face recognition as an example. Instead of treating computational neurons as hidden units whose parameters are determined by end-to-end optimization, we interpret computational neurons as dimension reduction units, which are derived from the statistics of input data, and the non-linear activation operation as a rectifier used to resolve the sign confusion problem. Multiple convolutional layers are viewed as the cascade of multiple Saab (Subspace approximation with adjusted bias) transforms, and Saab coefficients can be used as features for decision making. We apply and compare several classifiers (e.g., the Support Vector Machine, the Least-Squared Regression and the Random Forest) in the high-dimensional feature space to achieve the face recognition task. Finally, insights will be drawn from this pioneering endeavor.

Biography

Dr. C.-C. Jay Kuo received his Ph.D. degree from the Massachusetts Institute of Technology in 1987. He is now with the University of Southern California (USC) as Director of the Media Communications Laboratory and Distinguished Professor of Electrical Engineering and Computer Science. His research interests are in the areas of media computing, compression and understanding. Dr. Kuo was the Editor-in-Chief for the IEEE Trans. on Information Forensics and Security in 2012-2014. Dr. Kuo received the 1992 National Science Foundation Young Investigator (NYI) Award, the 1993 National Science Foundation Presidential Faculty Fellow (PFF) Award, the 2010 Electronic Imaging Scientist of the Year Award, the 2010-11 Fulbright-Nokia Distinguished Chair in Information and Communications Technologies, the 2011 Pan Wen-Yuan Outstanding Research Award, the 2014 USC Northrop Grumman Excellence in Teaching Award, the 2016 USC Associates Award for Excellence in Teaching, the 2016 IEEE Computer Society Taylor L. Booth Education Award, the 2016 IEEE Circuits and Systems Society John Choma Education Award, the 2016 IS&T Raymond C. Bowman Award, the 2017 IEEE Leon K. Kirchmayer Graduate Teaching Award and the 2019 IEEE Computer Society Edward J. McCluskey Technical Achievement Award. Dr. Kuo is a Fellow of AAAS, IEEE and SPIE. He has guided 150 students to their Ph.D. degrees and supervised 30 postdoctoral research fellows. Dr. Kuo is a co-author of 14 books, 280 journal papers, 940 conference papers and 39 patents.

Speaker

谭铁牛

中国科学院自动化所研究员

中国科学院院士、IEEE/IAPR Fellow

Title

Recent Advances and Future Directions of Biometric Recognition

Abstract

Biometrics as a reliable personal identification method has been widely used in mobile devices, digital payment, border control, video surveillance, etc. This talk will review the latest progress of the most popular biometric modalities and figure out some promising research directions for the next generation biometrics. Focus will be given on our recent work on iris, face and gait recognition with some interesting demos such as mobile iris recognition, iris recognition at a distance, GAN (Generative Adversarial Networks）based face image super-resolution and photorealistic face rotation, cross-view gait recognition, etc.

Biography

Tieniu Tan received MSc and PhD degrees in electronic engineering from Imperial College London, U.K and received BSc degree in electronic engineering from Xi'an Jiaotong University, China. He returned to China in 1998 and joined the National Laboratory of Pattern Recognition (NLPR), Institute of Automation of the Chinese Academy of Sciences (CAS), Beijing, China, where he is currently a Professor and the director of Center for Research on Intelligent Perception and Computing (CRIPAC), and was former director (1998-2013) of the NLPR and Director General of the Institute (2000-2007). He is Member of the Chinese Academy of Sciences, International Fellow of the UK Royal Academy of Engineering, Fellow of The World Academy of Sciences for the advancement of sciences in developing countries (TWAS), Corresponding Member of the Brazilian Academy of Sciences, and Fellow of the IEEE and IAPR (International Association for Pattern Recognition). He is currently also Deputy Director of Liaison Office of the Central People's Government in the Hong Kong S.A.R. He has published 14 edited books or monographs and more than 600 research papers in refereed international journals and conferences in the areas of image processing, computer vision and pattern recognition. His current research interests include biometrics, computer vision and pattern recognition.