The challenge of comparing digitally captured signatures registered with different software and hardware

Dynamic feature analysis of handwritten electronic signatures based on Fourier transform

The utilization of handwritten electronic signatures has expanded in various application scenarios, leading to an increased demand for identification. Unlike handwriting signatures, handwritten electronic signatures offer the advantage of extracting dynamic feature data, including writing pressure, velocity, and acceleration. In this study, the Fourier transform was employed to extract 18 characteristics from the time domain and frequency domain of writing pressure, velocity, and acceleration. The experimental findings revealed distinguishable differences between genuine signatures and random forgeries in writing pressure. However, no statistically significant differences were observed in writing velocity and writing acceleration. Moreover, significant differences were detected in most characteristics when comparing genuine signatures with freehand imitation forgeries and tracing imitation forgeries. The canonical discriminant analysis was performed between the genuine and Non-genuine signatures; the cross-validation estimated the discriminating power of these characteristics with a satisfactory result. The study proposed a new approach to analyzing handwritten electronic signatures using time-domain and frequency-domain characteristics and demonstrated its effectiveness in the examination.

Digitally captured signatures: A method for the normalization of force through calibration and the use of the zeta function

Digitally captured signatures (DCS) are entering the scope of the Forensic Handwriting Examiner (FHE), and the meaningful comprehension and comparison of the captured information is essential in order to proceed to the examination of authenticity of DCS. In DCS solutions, force is captured and presented as pressure levels, but the relation between exercised force and the assigned pressure levels is not provided by the manufacturers of these solutions. For this purpose, we constructed an experimental array that allows the correlation of exercised force and assigned pressure levels, in a combination of three different digitizers, six different styli and four different capturing software. This process let us calculate the correlation function that assigns pressure levels to force for each solution (called the Zeta Function). Through this process, it was observed that different solutions follow different Zeta functions. To address this problem, a methodology for normalization of captured data between different solutions was created and demonstrated, using the calculated Zeta function and its inverse.