In this work, a feature extraction method for off-line signature recognition and verification is proposed, described and validated. This approach is based on the exploitation of the relative pixel distribution over predetermined two and three-step paths along the signature trace. The proposed procedure can be regarded as a model for estimating the transitional probabilities of the signature stroke, arcs and angles. Partitioning the signature image with respect to its center of gravity is applied to the two-step part of the feature extraction algorithm, while an enhanced three-step algorithm utilizes the entire signature image. Fusion at feature level generates a multidimensional vector which encodes the spatial details of each writer. The classifier model is composed of the combination of a first stage similarity score along with a continuous SVM output. Results based on the estimation of the EER on domestic signature datasets and well known international corpuses demonstrate the high efficiency of the proposed methodology.