SetMargin loss applied to deep keystroke biometrics with circle packing interpretation
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...
| Published in: | Pattern Recognition |
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| Main Authors: | , , , , |
| Other Authors: | |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Elsevier
2022
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10486/700745 https://doi.org/10.1016/j.patcog.2021.108283 |
| Summary: | 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 This work has been supported by projects: PRIMA ( MSCA-ITN- 2019-860315 ), TRESPASS-ETN (MSCA-ITN-2019-860813), BIBECA (RTI2018-101248-B-I00 MINECO), edBB (UAM), and Instituto de In- genieria del Conocimiento (IIC). A. Acien is supported by a FPI fel- lowship from the Spanish MINECO |
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