1. The First ICB Competition on Iris Recognition
- Author
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Koichi Noda, Peihua Li, Nadia Othman, Valérian Némesin, Tieniu Tan, Man Zhang, Fernando Alonso-Fernandez, Jing Liu, Akanksha Joshi, Zhenan Sun, Wu Su, Edmundo Hoyle, Center for Research on Intelligent Perception and (Institute of Automation, Chinese Academy of Sciences), Department of Automation (University of Science and Technology of China), Zhuhai YiSheng Electronics Technology Co, LDT (.), Halmstad University, GSM (GSM), Institut FRESNEL (FRESNEL), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Département Electronique et Physique (TSP - EPH), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux (SAMOVAR), Centre National de la Recherche Scientifique (CNRS), Nihon System Laboratory (.), Dalian University of Technology, Universidade Federal do Rio de Janeiro (UFRJ), Centre for Development of Advanced Computing (.), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Département Electronique et Physique (EPH), and School of Information and Communication Engineering (Dalian University of Technology)
- Subjects
Computer science ,Speech recognition ,Iris recognition ,Signalbehandling ,02 engineering and technology ,Variation (game tree) ,urologic and male genital diseases ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,cardiovascular diseases ,Iris (anatomy) ,Signal processing ,business.industry ,urogenital system ,Rank (computer programming) ,fungi ,020206 networking & telecommunications ,Pattern recognition ,Image segmentation ,female genital diseases and pregnancy complications ,Identification (information) ,medicine.anatomical_structure ,Signal Processing ,020201 artificial intelligence & image processing ,Artificial intelligence ,Noise (video) ,business ,[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing - Abstract
International audience; Iris recognition becomes an important technology in our society. Visual patterns of human iris provide rich texture information for personal identification. However, it is greatly challenging to match intra-class iris images with large variations in unconstrained environments because of noises, illumination variation, heterogeneity and so on. To track current state-of-the-art algorithms in iris recognition, we organized the first ICB Competition on Iris Recognition in 2013 (or ICIR2013 shortly). In this competition, 8 participants from 6 countries submitted 13 algorithms totally. All the algorithms were trained on a public database(e.g. CASIA-Iris-Thousand[3]) and evaluated on an unpublished database. The testing results in terms of False Non-match Rate (FNMR) when False Match Rate (FMR) is 0:0001 are taken to rank the submitted algorithms
- Published
- 2014
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