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The inf uence of dataset quality on the results of behavioral biometric experiments

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This paper explores some aspects that are involved during the construction of reliable benchmark sample databases for novel behavioral biometric identification methods, such as the data quality, the recording patterns and the post processing procedures that may be applied on the data. A large collection of eye movement samples was employed as a test case. It was recorded under a variety of settings and processed with a number of different approaches. Our analysis reveals that there are specific features during the construction of a database that may significantly influence the final identification performance. It also leads on the establishment of some guidelines, which can be generalized on other behavioral biometric methods, regarding the factors that should be taken into consideration during the creation, the description and the processing of a database of biometric samples.
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... The randomness makes a random forest resilient to overfitting of the data [57]. A random forest was also adopted in previous work, such as [58] and [59]. We implemented the above classification and cross-validation process using the Scikit-Learn Python library [60], a commonly used machine learning open-source library. ...
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