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In conjunction with CVPR 2017, full day workshop on July 21 2017

Call for Papers

With a very security conscious society, biometrics based authentication and identification have become center of focus for many important applications as it is believed that biometrics can provide accurate and reliable identification. Biometrics research and technology continue to mature rapidly, driven by pressing industrial and government needs and supported by industrial and government funding. As the number and types of biometrics architectures and sensors increases, the need to disseminate research results increases as well. This workshop is intended to be positioned at the frontier of biometrics research and showcase the most excellent and advanced work underway at academic and private research organizations as well as government labs.

Many of the applications require higher level of accuracy performance not feasible with a single biometrics today. Additionally, it is also believed that fusing multiple biometrics will also improve wider coverage of population who may not be able to provide a single biometrics and also improve security of the systems in terms of spoof attacks. This workshop will address all aspects of research issues in different modes and levels of fusion of biometrics samples, sensing modes and modalities with a sole goal of improving performance of biometrics. Theoretical studies on sensor fusion techniques applied to biometrics authentication, recognition and performance are encouraged. The topics of interest are:



- Sensing; intensity, depth, thermal, pressure, time-series, exotic
- Face, finger, ear, eye, iris, retina, vein pattern, palm, gait, foot, exotic
- Biometric template computation and feature extraction, matching
- Data and performance baselines
- Evolution of standards, competitions and organized challenge problems
- Score level, decision level and feature level integration
- Architectures for integration, evidence integration
- Fusion based identification techniques
- Normalization techniques involved in fusion techniques
- Machine learning techniques in biometrics fusion
- Public databases and score files in multi-biometrics
- Application dependence personalization of multi-biometrics systems
- Theoretical studies in showing models for integration
- Performance modeling, prediction and evaluation of multi-biometrics systems
- Security improvement assessment for multi-biometrics systems

Overall Meeting Sponsors

Computer Vision Foundation IEEE Computer Society