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ISCOPE '98 Proceedings of the Second International Symposium on Computing in Object-Oriented Parallel Environments
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Java-ML: A Machine Learning Library
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LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Scikit-learn: Machine Learning in Python
The Journal of Machine Learning Research
Counter-measures to photo attacks in face recognition: A public database and a baseline
IJCB '11 Proceedings of the 2011 International Joint Conference on Biometrics
An open source framework for standardized comparisons of face recognition algorithms
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
LBP - TOP based countermeasure against face spoofing attacks
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume Part I
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Bob is a free signal processing and machine learning toolbox originally developed by the Biometrics group at Idiap Research Institute, Switzerland. The toolbox is designed to meet the needs of researchers by reducing development time and efficiently processing data. Firstly, Bob provides a researcher-friendly Python environment for rapid development. Secondly, efficient processing of large amounts of multimedia data is provided by fast C++ implementations of identified bottlenecks. The Python environment is integrated seamlessly with the C++ library, which ensures the library is easy to use and extensible. Thirdly, Bob supports reproducible research through its integrated experimental protocols for several databases. Finally, a strong emphasis is placed on code clarity, documentation, and thorough unit testing. Bob is thus an attractive resource for researchers due to this unique combination of ease of use, efficiency, extensibility and transparency. Bob is an open-source library and an ongoing community effort.