Open Source Software

Biometrics like any applied pattern recognition and machine learning research contains experimental results on real-world data. Results are typically summarized as a set of tables and figures, allowing the comparison of various methods. Unfortunately, result reproducibility is often an overlooked feature of original research publications, competitions, or benchmark evaluations. The main reason for such a gap is the complexity on the development of software associated with these reports. Software frameworks are difficult to install, maintain, and distribute, while scientific experiments often consist of many steps and parameters that are difficult to report. The increasingly rising complexity of research challenges make it even more difficult to reproduce experiments and results. As a consequence it is foremost importance to promote any actions towards reproducible research such as sharing Open Source software.

S. Marcel, A. Anjos and R. Lessmann

Literature

Continuously Reproducing Toolchains in Pattern Recognition and Machine Learning Experiments; André Anjos, Manuel Günther, Tiago de Freitas Pereira, Pavel Korshunov, Amir Mohammadi and Sébastien Marcel; International Conference on Machine Learning Workshop, 2017

Explanation (*)

Usage: Academic/Commercial, 1: On Request, 2: unkown