RAISING THE PERFORMANCE OF AUTOMATIC SIGNATURE VERIFICATION OVER THAT OBTAINABLE BY USING THE BEST FEATURE SET

Abstract

For decades, published works on Automatic Signature Verification (ASV) that use threshold-based decision, depended on using one feature set for verification. Some researchers selected this feature set based on their experience, and others selected it using some feature selection algorithms that can select the best feature set (gives the highest performance). In practical systems, the signature data could be noisy, and recognition of the check writer in multi-signatory accounts is required. Due to the error caused by such requirements and data quality, improving the performance becomes a necessity. In this paper, a new technique for ASV decision making use of Multi-Sets of Features (MSF) is introduced. The new technique and its motivation are explained, and a precise evaluation of its efficiency is made. The experimental results have shown that the new technique gives important improvement in forgery detection and in the overall performance. This technique which was developed within an integrated plan of building a commercial offline ASV system to work in the actual USA banks environment was tested during the prototyping period with about 1000 signature samples, and has already been in use for years as a component of a cooperative decision making ASV system that tests over a million check signature every day without any false acceptance (False Acceptance = 0).