CCBR 2018 乌鲁木齐

CCBR 2018 SPEAKERS

 嘉宾信息

 

特邀嘉宾


 

Anil K. Jain

Professor, Michigan State University, America

 
     Anil K. Jain教授是美国密歇根州立大学计算机科学与工程系和电子与计算机工程系杰出教授,研究领域包括模式识别、计算机视觉和生物特征识别,是多个国际著名学术组织如ACM、IEEE、AAAS、IAPR、SPIE等的会员。曾担任IEEE Tran. PAMI等期刊的主编,目前已经出版了《Handbookof Face Recognition》、《Handbook of Fingerprint Recognition》和《Handbook of Multibiometrics 》等多部专著,以及数百篇高水平学术论文,其中包括《Nature》论文1篇,IEEE Tran. PAMI论文95篇。Jain教授曾被评为全球计算机学科论文引用率最高的学者,他在人脸识别、指纹识别等方面的多项研究成果被NEC、Morpho等国际生物特征识别公司使用,在学术界和工业界具有极高的知名度和影响力。
    Anil Jain is a Distinguished Professor of Computer Science at Michigan State University. He is a Fellow of the ACM and IEEE and is a recipient of Guggenheim, Humboldt, Fulbright, and King-Sun Fu awards. He served as editor-in-chief of the IEEE Transactions on Pattern Analysis and Machine Intelligence and was a member of the United States Defense Science Board, Forensic Science Standards Board and AAAS latent fingerprint study. Jain is a member of the U.S. National Academy of Engineering and the Indian National Academy of Engineering.

    

     Title:From the Edge of Biometrics: What’s Next?
   Abstract:Biometric recognition refers to the automated recognition of individuals based on their biological and behavioral traits such as fingerprint, face, iris, and voice. The first scientific paper on automated fingerprint matching was published by Trauring (1963). Since then progress in the field has enabled biometric systems to accurately recognize individuals in real-time in applications ranging from unlocking personal smartphones to international border crossings. Despite this progress, a number of challenges and lack of understanding continue to inhibit the full potential of biometrics. In this talk I would like to share with you some of these challenges, requirements, opportunities for basic and applied research, and few ongoing projects in my laboratory.

    

 

Qiang Ji

Professor, Rensselaer Polytechnic Institute, America

 
     纪强(Qiang Ji)教授为国际著名的计算机视觉和情感计算专家、IEEE高级会员,曾在2009年1月至2010年8月期间曾任美国国家科学基金(NSF)计算机视觉和机器学习项目主任。纪教授目前为国际著名计算机视觉和情感计算研究机构RPI智能系统实验室(Intelligent Systems Laboratory, ISL)主任,在其主持下,已完成来自NSF,NIH, DARPA, ONR, ARO和AFOSR的项目40多项,并与多个工业界知名公司有密切合作关系。纪教授目前已在国际知名杂志及会议上发表学术论文达160余篇,论文引用次数达3000余次,H index为33,并多次获得重大学术奖项。纪教授身兼多个著名计算机视觉和情感计算相关期刊的编辑,其中包括IEEE Transactions on Affective Computing以及IEEE Transactions on Pattern Analysis and Machine Intelligence,并多次担任众多国际会议和论坛的程序委员会主席,技术主席和程序委员会委员等职务。
     Qiang Ji received his Ph.D degree in Electrical Engineering from the University of Washington. He is currently a Professor with the Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute (RPI). From 2009 to 2010, he served as a program director at the National Science Foundation (NSF), where he managed NSF’s computer vision and machine learning programs. He also held teaching and research positions with the Beckman Institute at University of Illinois at Urbana-Champaign; the Robotics Institute at Carnegie Mellon University; the Dept. of Computer Science at University of Nevada; and the Air Force Research Laboratory. Prof. Ji currently serves as the director of Intelligent Systems Laboratory (ISL) at RPI.
     Prof. Ji's research interests are in computer vision, probabilistic graphical models, machine learning, and their applications in various fields. He has published over 230 papers in peer-reviewed journals and conferences, and has received multiple awards for his work. Prof. Ji is an editor on several related IEEE and international journals and he has served as a general chair, program chair, technical area chair, and program committee member for numerous international conferences/workshops. Prof. Ji is a fellow of the IEEE and the IAPR.

    

    Title:Facial Landmark Detection Under Challenging Conditions
   Abstract: Facial landmark detection (a.k.a facial alignment) is a fundamental step for facial biometrics such as facial recognition, facial verification, and iris recognition. In this talk, I will first summarize different categories of methods for facial landmark detection. I will then discuss our recent work on facial landmark detection under challenging conditions, including significant facial and head pose variation and facial occlusion. My talk will end with a summary of the latest developments in facial landmark detection, the remaining challenges, and possible solutions.
 

Gang Hua

Principal Researcher/Research Manager, Microsoft Research

 
     Gang Hua is a Principal Researcher/Research Manager at the Microsoft Cloud and AI Group. His research focuses on computer vision, pattern recognition, machine learning, robotics, towards general Artificial Intelligence. He was an Associate Professor of Computer Science at Stevens Institute of Technology between 2011 and 2015.
     He was a Research Staff Member (2010-2011) with IBM Research T. J. Watson Center, a Senior Researcher (2009-2010) with Nokia Research Center Hollywood, and a Scientist (2006-2009) with Microsoft Live labs Research. He received his Ph.D. degree in ECE from Northwestern University in 2006.
    He is the recipient of the 2015 IAPR Young Biometrics Investigator Award for his contribution to Unconstrained Face Recognition from Images and Videos, and a recipient of the 2013 Google Research Faculty Award. He will serve as a Program Chair for CVPR'2019 and CVPR’2022. He has served as Area Chairs for many top international conferences. He is currently an Associate Editor in Chief for CVIU, and Associate Editors for IJCV, IEEE T-IP, IEEE T-CSVT, IEEE Multimedia, and MVA.
  He has published 150 peer reviewed papers in top conferences such as CVPR/ICCV/ECCV, and top journals such as T-PAMI and IJCV. He holds 19 issued U.S Patents and has more than 20 U.S. Patents Pending. He is a Senior Member of IEEE, an IAPR Fellow, and an ACM Distinguished Scientist.

    

    Title:Efficient, Accurate, and Robust Face Recognition
   Abstract: Face recognition has matured to a stage to support a lot of commercial applications. Nevertheless, there are still lots of challenges need to be addressed. I will start the talk with a brief status quo of face recognition research and development in both academia and industry. Then, I will summarize some of our recent research works on efficient and accurate face detection and recognition, and how we may deal with adversarial attacks using an identity preserving deep generative model. I will conclude my talks with recent trends in the research community in face recognition..
 

郑方

清华大学,博士、教授、博士生导师

 
     清华大学语音和语言技术中心主任,北京得意音通技术有限责任公司董事长、得意音通信息技术研究院院长;IEEE高级会员、APSIPA(亚太区信号与信息处理联合会)副主席、中国声学学会理事、中国中文信息学会理事、中国中文信息学会语音专委会主任、中国计算机学会语音对话与听觉专业组副主任等。 郑方博士从事语音语言处理和生物特征识别的研发近30年,是全国安防标委会人体生物特征识别应用分委会副主任委员、中文语音交互技术标准工作组声纹识别专题组组长、全国信标委生物特征识别分委会委员等,是声纹相关所有的国家和行业标准的主要起草者。

    

    标题:更高安全保障、更低隐私泄露——语音技术用于身份认证的理论与实践
   摘要: 随着AI的飞速发展及无线互联应用的快速普及,身份识别尤其是在自助及线上这些无监督(无人监督及无法监督)情况下显得非常重要。本报告指出在无监督情况下进行身份认证所必需满足的技术要求,对语音信号的特点进行了理论分析,对语音处理技术所能达到的技术水平以及语音信号用于无监督身份识别的实践进行了简单介绍。理论和实践均表明,语音信号用于无监督身份识别,在防假体攻击、检测用户真实意图及追溯用户行为等方面优势明显,具有低成本、低隐私(泄露)和高安全的特点。
 

王文峰

中国电子技术标准化研究院物联网研究中心副主任、物联网标准与应用工业和信息化部重点实验室主任

 
     王文峰,中国电子技术标准化研究院物联网研究中心副主任、物联网标准与应用工业和信息化部重点实验室主任,主要从事生物特征识别技术、物联网技术、无人机身份识别、智能卡技术、RFID技术、条码/二维码技术、实时定位技术等领域标准标准化研究工作;负责全国信息技术标准化技术委员会卡和安全设备身份识别分技术委员会、生物特征识别分技术委员会实时定位系统标准制定工作组等组织的秘书处工作。

    

     其他职位:
  • 国家重要产品追溯标准化总体专家组专家;
  • 国家标准化管理委员会国家标准技术评估专家。
  • 工业和信息化部工程系列高级专业技术职务任职资格评审委员会计量专业分评会委员。
  • ISO/IEC JTC1/SC17卡和安全设备身份识别注册专家;
  • ISO/IEC JTC1/SC31自动识别和数据采集分委会注册专家;
  • ISO/IEC JTC1/SC37生物特征识别分委会注册专家;
  • ISO/PC 308监管链项目委员会注册专家。

    

    标题:生物特征识别技术及标准化
   摘要: 生物特征识别技术与应用;生物特征识别应用存在的问题;生物特征识别标准化组织与标准化状况;生物特征识别注册及检测。