Visualization of Handwritten Signatures Based on Haptic Information

The problem of user authentication is a crucial component of many solutions related to defense and security. The identification and verification of users allows the implementation of technologies and services oriented to the intended user and to prevent misuse by illegitimate users. It has become an essential part of many systems and it is used in several applications, particularly in the military. The handwritten signature is an element intrinsically endowed with specificity related to an individual and it has been used extensively as a key element in identification/authentication. Haptic technologies allow the use of additional information like kinesthetic and tactile feedback from the user, thus providing new sources of biometric information that can be incorporated within the process in addition to the traditional image-based sources. While work had been done on using haptic information for the analysis of handwritten signatures, most efforts have been oriented to the direct use of machine learning techniques for identification/verification. Comparatively fewer targeted information visualization and understanding the internal structure of the data. Here a variety of techniques are used for obtaining representations of the data in low dimensional spaces amenable to visual inspection (two and three dimensions). The approach is unsupervised, although for illustration and comparison purposes, class information is used as qualitative reference. Estimations of the intrinsic dimension for the haptic data are obtained which shows that low dimensional subspaces contains most of the data structure. Implicit and explicit mappings techniques transforming the original high dimensional data to low dimensional spaces are considered. They include linear and nonlinear, classical and computational intelligence based methods: Principal Components, Sammon mapping, Isomap, Locally Linear Embedding, Spectral Embedding, t-Distributed Stochastic Neighbour Embedding, Generative Topographic Mapping, Neuroscale and Genetic Programming. They provided insight about common and specific characteristics found in haptic signatures, their within/among subjects variability and the important role of certain types of haptic variables. The results obtained suggest ways how to design new representations for identification and verification procedures using tactile devices.