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RBFN for scikit-learn

Radial Base Function Networks as scikit-learn classifier. Implementation for the paper:

Schröder, Christoph, Sahar Mahdie Klim Al Zaidawi, Martin H.U. Prinzler, Sebastian Maneth, and Gabriel Zachmann. ”Robustness of Eye Movement Biometrics Against Varying Stimuli and Varying Trajectory Length”. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–7. CHI ’20. Honolulu, HI, USA: Association for Computing Machinery, 2020. https://doi.org/10.1145/3313831.3376534.

This implementation follows the description from Anjith George and Aurobinda Routray. 2016. ”A score level fusion method for eye movement biometrics”. Pattern Recognition Letters 82 (2016), 207–215. DOI: http://dx.doi.org/10.1016/j.patrec.2015.11.020

Usage

from sklearn import datasets
from sklearn.metrics import classification_report, accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

from rbfn import Rbfn

iris = datasets.load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)
scaler = StandardScaler()

clf = Rbfn()
clf.fit(scaler.fit_transform(X_train), y_train)

y_pred = clf.predict(scaler.transform(X_test))
print(classification_report(y_test, y_pred))
print("\tAccuracy: %1.3f" % accuracy_score(y_test, y_pred))

Citation

If you use this code in a scientific publication, we would appreciate citations:

Schröder, Christoph, Sahar Mahdie Klim Al Zaidawi, Martin H.U. Prinzler, Sebastian Maneth, and Gabriel Zachmann. “Robustness of Eye Movement Biometrics Against Varying Stimuli and Varying Trajectory Length””. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–7. CHI ’20. Honolulu, HI, USA: Association for Computing Machinery, 2020. https://doi.org/10.1145/3313831.3376534.

Bibtex entry

@inproceedings{Schroeder-2020-RobustnessEye,
  author = {Schröder, Christoph and Al Zaidawi, Sahar Mahdie Klim and Prinzler, Martin H.U. and Maneth, Sebastian and Zachmann, Gabriel},
  title = {Robustness of Eye Movement Biometrics Against Varying Stimuli and Varying Trajectory Length},
  year = {2020},
  isbn = {9781450367080},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3313831.3376534},
  doi = {10.1145/3313831.3376534},
  booktitle = {Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems},
  pages = {1–7},
  numpages = {7},
  keywords = {eye tracking, gaze detection, eye movement biometrics},
  location = {Honolulu, HI, USA},
  series = {CHI ’20}
}

License

This original work is copyright by University of Bremen. Any software of this work is covered by the European Union Public Licence v1.2. To view a copy of this license, visit eur-lex.europa.eu

Any other assets (3D models, documents, etc.) are covered by the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit creativecommons.org.

If you use any of the assets or software to produce a publication, then you must give credit and put a reference in your publication.

If you would like to use our software in proprietary software, you can obtain an exception from the above license (aka. dual licensing). Please contact zach at cs.uni-bremen dot de.