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CIBIM - Special Session 2015 : 2015 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (IEEE CIBIM'15) - SPECIAL SESSION Machine Learning Techniques for Fingerprint Biometrics


When Dec 7, 2015 - Dec 10, 2015
Where Cape Town, South Africa
Submission Deadline Jul 14, 2015
Notification Due Sep 4, 2015
Final Version Due Oct 4, 2015

Call For Papers


2015 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management

Machine Learning Techniques for Fingerprint Biometrics

Cape Town, South Africa ̶ December 7-10, 2015

Fingerprint is one of the most commonly used traits in biometric application due to its high acceptability
and distinctiveness. Machine learning techniques and, in general, computational intelligence approaches,
are widely adopted in establishing identity based on fingerprint biometrics and also to overcome
non-idealities (intra-class variations) typically present in fingerprint samples, acquired using touch-based
and touchless sensors. Typical areas related to fingerprint biometrics include enhancement, feature
extraction, classification, indexing and anti-spoofing. In this context, computational intelligence/ machine
learning approaches play an important role in performing complex nonlinear computations by creating
models from the training data. These approaches are based on supervised as well as unsupervised training
techniques. Examples of well-known machine learning approaches are artificial neural networks, support
vector machines, clustering methods, sparse dictionary learning, decision trees, and evolutionary
algorithms. This special session present an opportunity for discussing advances in machine learning
techniques applied to fingerprint biometrics, both theoretical as well as applicative.

In the context of fingerprint recognition systems, topics of interest include, but are not limited to:
- Image Enhancement
- Feature Extraction (Level 1, Level 2, and Level 3)
- Matching
- Classification and Indexing
- Distortion Correction
- Processing of Latent Images
- Processing of Three-Dimensional Samples
- Processing of Touchless Samples
- Quality Assessment
- Adaptive Biometric Recognition
- Anti-Spoofing / Liveness detection
- Fusion Techniques

Paper submission: July 14, 2015 [Extended]
Notification to authors: September 4, 2015
Final submission: October 4, 2015

Ruggero Donida Labati, Università degli Studi di Milano, Italy
Ajita Rattani, University of Missouri- Kansas City, MO, USA

Fabio Scotti, Università degli Studi di Milano, Italy
Qinghan Xiao, Defence Research and Development Canada, Canada
David Zhang, The Hong Kong Polytechnic University, Hong Kong

Enrique Muñoz Ballester, Università degli Studi di Milano, Italy

Herve Chabanne, ParisTech, France
Ruggero Donida Labati, Università degli Studi di Milano, Italy
Gerry Vernon Dozier, North Carolina A&T State University, USA
Yingzi (Eliza) Du, University-Purdue University Indianapolis, USA
Giorgio Fumera, University of Cagliari, Italy
Eric Granger, Ecole de technologie superieure, Canada
Laurent Heutte, Universite de Rouen, France
Kevin Jia, IGT, USA
Kailash J Karande, SKN Sinhgad College of Engineering, India
Hale Kim, Inha University, Korea
Adams Wai-Kin Kong, Nanyang University, Singapore
Wenxin Li, Peking University, China
Zheng Liu, Toyota Technological Institute, Japan
Gian Luca Marcialis, University of Cagliari, Italy
Damodaran Nedumaran, University of Madras, India
Hugo Proenca, University of Beira Interior, Portugal
Ajita Rattani, University of Cagliari, Italy
Robert Sabourin, Ecole de Technologie Superieure, Canada
Seref Sagiroglu, Gazi University, Ankara, Turkey
Mario Savastano, National Research Council of Italy, Italy
Nicole Vincent, Universite Rene Descartes, France
Lidong Wang, Mississippi Valley State University, USA
Yong Xu, Harbin Institute of Technology, China
Svetlana N. Yanushkevich, University of Calgary, Canada
Wei-Yun Yau, Institute for Information Research, Singapore
Lei Zhang, The Hong Kong Polytechnic University, Hong Kong
Huiyu Zhou, Queen's University Belfast, UK

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