  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.
