Signature can be seen as an individual characteristic of a person which, if modeled with precision can be used for his/her validation. An automated signature authentication technique saves valuable time and money. The paper is primarily focused on skilled forgery detection. It emphasizes on the extraction of the critical regions which are more prone to mistakes and matches them following a modular graph matching approach. The technique is robust and takes care of the inevitable intra-personal variations. The results show significant improvement over other approaches for detecting skilled forgery.