Offline Handwritten Signature Verification - Literature Review <s> Interest point matching <s> In this article, a new approach to offline signature verification, based on a general-purpose wide baseline matching methodology, is proposed. Instead of detecting and matching geometric, signature-dependent features, as it is usually done, in the proposed approach local interest points are detected in the signature images, then local descriptors are computed in the neighborhood of these points, and afterwards these descriptors are compared using local and global matching procedures. The final verification is carried out using a Bayes classifier. It is important to remark that the local interest points do not correspond to any signature-dependent fiducial point, but to local maxima in a scale-space representation of the signature images. The proposed system is validated using the GPDS signature database, where it achieves a FRR of 16.4% and a FAR of 14.2%. <s> BIB001 </s> Offline Handwritten Signature Verification - Literature Review <s> Interest point matching <s> The purpose of writing this paper is two-fold. First, it presents a novel signature stability analysis based on signature's local / part-based features. The Speeded Up Local features (SURF) are used for local analysis which give various clues about the potential areas from whom the features should be exclusively considered while performing signature verification. Second, based on the results of the local stability analysis we present a novel signature verification system and evaluate this system on the publicly available dataset of forensic signature verification competition, 4NSigComp2010, which contains genuine, forged, and disguised signatures. The proposed system achieved an EER of 15%, which is considerably very low when compared against all the participants of the said competition. Furthermore, we also compare the proposed system with some of the earlier reported systems on the said data. The proposed system also outperforms these systems. <s> BIB002
Interest point matching methods, such as SIFT (Scale-Invariant Feature Transform) and SURF (Speeded Up Robust Features) has been largely used for computer vision tasks, such as object recognition and 3D reconstruction. Some authors have created feature extraction techniques on top of these methods, to enable their use in the Signature verification task. Ruiz-del-Solar et al. BIB001 used SIFT to extract local interest points from both query and reference samples to build a writer-dependent classifier. After extracting interest points from both images, they generated a set of 12 features, using information such as the number of SIFT matches between the two images, the processing time (as a measure of complexity of the matching process), along with other information from the transformations. They then trained a Bayes classifier with these features. Using skilled forgeries for training, they obtained good results on the GPDS dataset. Malik et al. BIB002 used SURF features to classify among genuine signatures, forgeries and disguised signatures. They first used SURF to extract interest points in the signature images, and used these features to assess the local stability of the signatures (i.e. find parts of the genuine signatures that are more stable over time). During classification, only the stable interest points are used for matching. The number of keypoints in the query image, and the number of matched keypoints were used to classify the signature as genuine or forgery.