首页|SetMargin loss applied to deep keystroke biometrics with circle packing interpretation
SetMargin loss applied to deep keystroke biometrics with circle packing interpretation
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NSTL
Elsevier
This work presents a new deep learning approach for keystroke biometrics based on a novel Distance Metric Learning method (DML). DML maps input data into a learned representation space that reveals a "semantic" structure based on distances. In this work, we propose a novel DML method specifically de-signed to address the challenges associated to free-text keystroke identification where the classes used in learning and inference are disjoint. The proposed SetMargin Loss (SM-L) extends traditional DML ap-proaches with a learning process guided by pairs of sets instead of pairs of samples, as done traditionally. The proposed learning strategy allows to enlarge inter-class distances while maintaining the intra-class structure of keystroke dynamics. We analyze the resulting representation space using the mathemati-cal problem known as Circle Packing, which provides neighbourhood structures with a theoretical max-imum inter-class distance. We finally prove experimentally the effectiveness of the proposed approach on a challenging task: keystroke biometric identification over a large set of 78,0 0 0 subjects. Our method achieves state-of-the-art accuracy on a comparison performed with the best existing approaches. (c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
NSTL
Elsevier
