This work presents a feature extraction method for writer verification based on their handwriting. Motivation for this work comes from the need of enchancing modern eras security applications, mainly focused towards real or near to real time processing, by implementing methods similar to those used in signature verification. In this context, we have employed a full sentence written in two languages with stable and predefined content. The novelty of this paper focuses to the feature extraction algorithm which models the connected pixel distribution along predetermined curvature and line paths of a handwritten image. The efficiency of the proposed method is evaluated with a combination of a first stage similarity score and a continuous SVM output distribution. The experimental benchmarking of the new method along with others, state of the art techniques found in the literature, relies on the ROC curves and the Equal Error Rate estimation. The produced results support a first hand proof of concept that our proposed feature extraction method has a powerful discriminative nature.