ABSTRACT
Biometric researchers have historically seen signature duplication as a procedure relevant to improving the performance of automatic signature verifiers. Different approaches have been proposed to duplicate dynamic signatures based on the heuristic affine transformation, nonlinear distortion and the kinematic model of the motor system. The literature on static signature duplication is limited and as far as we know based on heuristic affine transforms and does not seem to consider the recent advances in human behavior modeling of neuroscience. This paper tries to fill this gap by proposing a cognitive inspired algorithm to duplicate off-line signatures. The algorithm is based on a set of nonlinear and linear transformations which simulate the human spatial cognitive map and motor system intra-personal variability during the signing process. The duplicator is evaluated by increasing artificially a training sequence and verifying that the performance of four state-of-the-art off-line signature classifiers using two publicly databases have been improved on average as if we had collected three more real signatures.
ABSTRACT
In this paper a model called symbolic function network (SFN) is introduced; that is based on using elementary functions (for example powers, the exponential function, and the logarithm) as building blocks. The proposed method uses these building blocks to synthesize a function that best fits the training data in a regression framework. The resulting network is of the form of a tree, where adding nodes horizontally means having a summation of elementary functions and adding nodes vertically means concatenating elementary functions. Several new algorithms were proposed to construct the tree based on the concepts of forward greedy search and backward greedy search, together with applying the steepest descent concept. The method is tested on a number of examples and it is shown to exhibit good performance.
