Handwriting verification – Comparison of a multi-algorithmic and a multi-semantic approach

In this paper, a comparison of an existing multi-algorithmic and a new multi-semantic fusion approach for biometric online handwriting user verification is presented. First, in order to improve the authentication performance of a biometric online handwriting system four classification algorithms are combined using several weighting strategies for matching score level fusion. Second, based on the best two algorithms and the best weighting strategy found during the test of the multi-algorithmic approach, a new multi-semantic fusion approach using a pair wise combination of four semantics on matching score level is proposed. As semantics we understand alternative handwritten contents (e.g. symbols) in addition to signatures. We show that both fusion approaches, multi-algorithmic and multi-semantic, can lead to a fusion result which is better than the result of the best single algorithm or semantics involved. While the improvement for the multi-algorithmic system yields 19%, we observe more than 57% for the multi-semantic approach.