SetMargin loss applied to deep keystroke biometrics with circle packing interpretation. (February 2022)

• Record Type: Journal Article
• Authors: Morales, Aythami; Fierrez, Julian; Acien, Alejandro; Tolosana, Ruben; Serna, Ignacio
• Is Part Of: Pattern recognition. Volume 122(2022)
• Publication Date: 2022-02
• Subjects: Keystroke biometrics -- Circle packing -- Deep learning -- DML; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2021.108283
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A new keystroke identification method based on a novel loss function. We introduce the Circle Packing problem as a novel way to gain insights into learned feature spaces. Experiments on open-set keystroke biometric database with 78, 000 subjects. 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.