A New Decision Making Approach for Improving the Performance of Automatic Signature Verification Using Multi-sets of Features

Abstract

So far, Automatic Signature Verification (ASV) approaches using a threshold-based decision have depended on one feature set for distance measure and a threshold on this distance measure for verification. The best performance that can be reached in this case is the one obtained by using the best feature set (bfs). In this paper, we introduce a new decision making approach for ASV that uses Multi-Sets of Features (MSF). The MSF provides higher performance than that obtainable by using the bfs, with better forgery detection. The improvement is seen to be significant because it recovers some lost effectiveness and can add it to that of the bfs. This gain in effectiveness is highly desirable when we deal with signatures of high value documents.