SetMargin Loss applied to Deep Keystroke Biometrics with Circle Packing Interpretation

• URL: http://arxiv.org/abs/2109.00938v1
• Date: Thu, 2 Sep 2021 13:26:57 GMT
• Title: SetMargin Loss applied to Deep Keystroke Biometrics with Circle Packing Interpretation
• Authors: Aythami Morales and Julian Fierrez and Alejandro Acien and Ruben Tolosana and Ignacio Serna
• Abstract summary: This work presents a new deep learning approach for keystroke biometrics based on a novel Distance Metric Learning method (DML) We prove experimentally the effectiveness of the proposed approach on a challenging task: keystroke biometric identification over a large set of 78,000 subjects.
• Score: 67.0845003374569
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 designed 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 approaches 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 mathematical problem known as Circle Packing, which provides neighbourhood structures with a theoretical maximum 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 78,000 subjects. Our method achieves state-of-the-art accuracy on a comparison performed with the best existing approaches.

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