CIBIM 2011 - CIBIM 2011 2011 IEEE Workshop on Computational Intelligence in Biometrics and Identity Management

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Category CIBIM 2011

Deadline: October 31, 2010 | Date: April 11, 2011-April 15, 2011

Venue/Country: Paris, France

Updated: 2010-08-02 13:27:12 (GMT+9)

Call For Papers - CFP

2011 IEEE Workshop on Computational Intelligence in Biometrics and Identity Management

Biometric technology is the technology of the 21st century which uses measurable physiological or behavioural characteristics to reliably distinguish one person from another. The technology is fast gaining popularity as means of personal identification and verification for different commercial, government and law enforcement applications. Since biometric information cannot be captured in precisely the same way twice, biometric matching is always a “fuzzy comparison”. This feature makes computational intelligence (CI), which is primarily based on artificial intelligence, neural networks, fuzzy logic, evolutionary computing, etc., an ideal solution for addressing biometric problems. The main objective of this workshop is to bring together the leading researchers to exchange the latest theoretical and experimental CI solutions in biometrics and identity management. This event will provide an interdisciplinary forum for research scientists, system developers and students from around the world to discuss the latest advances in the field of Computational Intelligence and its application to real world problems in biometrics and identity management. The submission needs to deal with computational intelligence in biometrics.

Topics

Topics of interest include but are certainly not limited to:

CI-based biometric algorithms, techniques and systems

Machine learning, neural-networks and artificial intelligence methods in biometrics and identity management

Biometric solutions for physical and logical securities

Biometric smart ID, RFID ePassport, biometric authentication and identity management

Biometric information privacy and data security

Covert and unconstrained biometrics

Multiple biometrics and multi-modal biometrics information fusion

Biometric anti-spoofing and liveness detection

Mobile biometric devices and embedded biometric systems

Biometric performance, assurance, and interoperability testing

Symposium Co-Chairs

Qinghan Xiao (qinghan.xiaodrdc-rddc.gc.ca), Defence R&D, Canada

David Zhang (csdzhangcomp.polyu.edu.hk), Hong Kong Polytechnic University, China

Fabio Scotti (fscottidti.unimi.it), University of Milan, Italy

Program Committee

Hervé Chabanne, Morpho & Télécom ParisTech, France

Ke Chen, University of Manchester, UK

Eliza Du, Indiana University-Purdue University Indianapolis, USA

Jianjiang Feng, Tsinghua University, China

Eric Granger, École de technologie supérieure, Montreal, Canada

Kevin Jia, IGT, USA

Adams Wai-Kin Kong, Nanyang University, Singapore

Wenxin Li, Peking University, China

Hugo Proença, University of Beira Interior, Portugal

Evangelia Micheli-Tzanakou, Rutgers University, USA

Seref Sagıroglu, Gazi University, Ankara, Turkey

Mario Savastano, National Research Council of Italy

Jie Tian, Chinese Academy of Sciences, China

Jeffrey Voas, Science Applications International Corporation, USA

Jia-Ching Wang, National Cheng Kung University, Tainan, Taiwan

Yong Xu, Harbin Institute of Technology, China

Xin Yang, Chinese Academy of Sciences, China

Svetlana N. Yanushkevich, University of Calgary, Alberta, Canada

Special Sessions

#1. Adaptive Classification Systems for Biometric Recognition

The recognition of individuals based on their biometric traits provides a powerful alternative to traditional authentication schemes presently applied in a multitude of security and surveillance systems. However, the performance of state-of-the-art neural and statistical classifiers employed in biometric recognition systems typically decline in practice because they face complex operational environments that change over time, and because they are designed a priori using limited and unbalanced data samples. In fact, biometric systems are typically designed with a limited set of training samples, and with static classification environments in mind. For accurate and timely recognition, biometric systems should allow for efficient adaptation in response to emerging knowledge and data.

In recent years, adaptive classification systems have been proposed to efficiently maintain up-to-date biometric models, and sustain a high level of accuracy in real-world biometric applications. These systems have the ability to evolve their parameters and architecture over time in response to new or changing input features, data samples, classes (i.e., individuals) and/or environments. Moreover, these systems play a central role in self-adapting and human-centric frameworks, where biometric systems are gradually designed and updated as the operational environment unfolds. Significant challenges must be overcome before such techniques can be successfully deployed for real-world biometric applications. The purpose of this session is to provide a scientific forum for researchers, engineers, system designers to present and discuss recent advances in the area of adaptive classification systems for biometric recognition and related technologies.

Topics

Suggested topics include as they apply to biometric recognition, but are not limited to:

Adaptive Pattern Recognition Methods, Systems and Technologies

Intelligent and Evolutionary Systems

Neural and Statistical Classifiers

Multi-Classifier Systems

Incremental Learning of Features, Data Samples and Classes

On-Line, Adaptive and Life-Long Learning

Selection and Fusion in Ensembles of Classifiers

Evolutionary Computation

Feature Extraction and Selection

Adaptation of Biometric Systems in Static and Dynamically-Changing Environments

Ambiguity and Novelty Detection

Methodologies for Evaluation of Adaptive Biometric Systems

Special Session Organizer and Chair

Eric Granger, Université du Québec, Montreal, Canada (eric.grangeretsmtl.ca)

#2 Decision-making Support for Biometric Systems

Decision-making support system (DMSS) has been known as an enabler of improving quality of decision. Biometric decision-making support is a potential application domain of DMSS because of the number of influencing factors and complexity of biometric systems. The aim of this session is to provide a scientific forum for researchers, engineers and computer scientists to discuss and report recent advantages in the area of artificial intelligence techniques for enhancing application of biometrics in civil, law enforcement, biomedical and other applications.

Topics

Original research in the area of biometric systems and applications is solicited, which may include, but is not limited to:

Artificial intelligence methods in biometrics

Agent based authentication systems

Reliability of biometric evidence

Bayesian and Dempster-Shafer decision-making for biometric systems

Fusion levels (rank, decision, sensor, feature and match-score)

Multibiometric system applications

All other aspects of decision-making in biometric application

Special Session Organizers and Chairs

Svetlana N. Yanushkevich, Biometric Technologies Laboratory, University of Calgary, Canada (syanshkucalgary.ca)

Vlad Shmerko, Biometric Technologies Laboratory, University of Calgary, Canada