This algorithm is a legacy one. The API has changed since its implementation. New versions and forks will need to be updated.

Algorithms have at least one input and one output. All algorithm endpoints are organized in groups. Groups are used by the platform to indicate which inputs and outputs are synchronized together. The first group is automatically synchronized with the channel defined by the block in which the algorithm is deployed.

Group: probes

Endpoint Name Data Format Nature
comparison_ids system/array_1d_text/1 Input
keystroke tutorial/atvs_keystroke/1 Input
probe_client_id system/text/1 Input
scores elie_khoury/string_probe_scores/1 Output

Group: templates

Endpoint Name Data Format Nature
template_client_id system/text/1 Input
template_id system/text/1 Input
model_template aythamimm/keystroke_model/8 Input

Parameters allow users to change the configuration of an algorithm when scheduling an experiment

Name Description Type Default Range/Choices
field Data field used to generate the feature template string given_name given_name, family_name, email, nationality, id_number, all_five
distance Distance to obtain the matching score string Modified Scaled Manhattan Scaled Manhattan, Modified Scaled Manhattan, Combined Manhattan-Mahalanobis, Mahalanobis + Nearest Neighbor

The code for this algorithm in Python
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For a given set of feature vectors and enrollment set, the modified manhattan scaled distance is obtained. See [1] for details.


I agree:

  1. to cite [1] in any paper of mine or my collaborators that makes any use of the software (codes) or data generated from these codes.

[1] A. Morales, M. Falanga, J. Fierrez, C. Sansone and J. Ortega-Garcia, "Keystroke Dynamics Recognition based on Personal Data: A Comparative Experimental Evaluation Implementing Reproducible Research ", in Proc. the IEEE Seventh International Conference on Biometrics: Theory, Applications and Systems, Arlington, Virginia, USA, September 2015

No experiments are using this algorithm.
This algorithm was never executed.
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