Handwritten Signature is a behavioral biometric trait which is extensively used for personal authorization. Signatures act as a strong authentication feature of the signer and thus, preserve their valuable assets such as authenticating bank cheques, attendance monitoring, property documents and other confidential documents. But, the manual verification of signatures is difficult job. Thus, an Automated Signature Verification System is required which will improve the authentication process and provide secure means for authorization of legal documents. In this book, Offline Signature Verification System and its various extracted features for forgery detection are discussed. GMM (Gaussian Mixture Model) technique is the important part of this book. GMM is a statistical method in which we have to cluster low level data with the help of several multidimensional Gaussian probability distributions. It allows the modeling of underlying statistics of sample data to be more flexible and precise. This work would be helpful for professionals and students/researchers who want to get an insight regarding how GMM Technique works for Offline Handwritten Signature Verification to detect forgery.