Offline Signature Verification using Locally Optimized Distance-based Classification

Abstract

Although handwritten signature verification has been extensively researched, it has not achieved an optimal classification accuracy rate. Therefore, efficient and accurate signature verification techniques are required since signatures are still widely used as a means of personal verification. This research work presents efficient distance-based classification techniques as an alternative to supervised learning classification techniques (SLTs). The Local Directional Pattern (LDP) feature extraction technique was used to analyze the effect of using several different distance-based classification techniques. The classification techniques tested, are the Euclidean, Manhattan, Fractional, weighted Euclidean, weighted Manhattan, weighted fractional distances and individually optimized resampling of feature vector sizes. The best accuracy, of 90.8%, was achieved through applying a combination of the weighted fractional distances and locally optimized resampling classification techniques to the Local Directional Pattern feature extraction. These results are compared with results from literature, where the same feature extraction technique was classified with SLTs. The distance-based classification was found to produce greater accuracy than the SLTs.