For decades, published Automatic Signature Verification (ASV) works depended on using one feature set. Some researchers selected this feature set based on their experience, and some others selected it using some feature selection algorithms that can
select the best
feature set (bfs). In practical systems, the documents containing the signatures could be noisy, and recognition of check writer in multi-signatory accounts is required. Due to the error caused by such requirements and data quality, improving the performance of ASV
becomes a necessity. In this paper, a new technique for ASV decision making using Multi-Sets of Features is introduced. The experimental results have shown that the introduced technique gives important improvement in forgery detection and in the overall performance of the system.
