Comparison of Biometric Authentication Software Techniques: GEFE vs. Angle Based Metrics
2016
- 19Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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Artifact Description
In this paper, we explore three alternatives for developing a biometric authentication software system. The first approach we will consider is a computer vision technique optimized by Genetic and Evolutionary Feature Extraction (GEFE); the second is Angle Based Metrics (ABM); and the third is Angle Based Metrics combined with Genetic and Evolutionary Computation (ABM + GEC). Each of these techniques are research areas which show promise in regards to being able to authenticate users based on their natural mouse movements. When applied to the same data set, the results of our experimentation indicate that both the ABM and ABM + GEC techniques are more accurate than GEFE in correctly verifying genuine users, as well as correctly rejecting impostors.
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