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When using tablet computers, smartphones, or digital pens, human users perform movements with a stylus or their fingers that can be analyzed by the kinematic theory of rapid human movements. In this paper, we present a user-centered system for signature verification that performs such a kinematic analysis to verify the identity of the user. It is one of the first systems that is based on a direct comparison of the elementary neuromuscular strokes which are detected in the handwriting. Taking into account the number of strokes, their similarity, and their timing, the string edit distance is employed to derive a dissimilarity measure for signature verification. On several benchmark datasets, we demonstrate that this neuromuscular analysis is complementary to a well-established verification using dynamic time warping. By combining both approaches, our verifier is able to outperform current state-of-the-art results in on-line signature verification.

The papers in this special section focus on handwriting and drawing processes for user-centered systems. The papers provide a wide and updated overview of the frontier of research in the field of humancentered systems based on drawing and handwriting processing. Through the papers, some of the most relevant directions of further research are highlighted with specific attention to components related to human–machine interaction. The Guest Editors hope that this issue brings forth the importance of automated systems related to automatic processing of drawing and handwriting.