Reliable offline signature verification with cascade classifier ensemble

Signature verification has been widely applied in financial and legal transactions for authentication and has attracted much attention in the academia and industries. In this paper, a two-stage cascade verification system is proposed to minimize the cost of wrong verifications. In the first stage, an improved local mean K-Nearest Neighbor is applied with two reliable parameters to measure the confidence level of the judgment for a testing sample. At the second stage, a multiple expert system is formed with Random Subspace method and the reliability of decisions is evaluated by majority voting. With a testing sample, the first stage classifier will make an evaluation about the confidence level of the verification. If it is reliable enough, the result will be final; otherwise, the sample is rejected by the first stage and passed on to the second for further assessment. In this case, the final result will be the outcome of the second stage when the result has been accepted. Otherwise, this testing sample will be rejected by the whole system. Comparing to a single classifier, the cascade system can reduce the rejection rate with a slight sacrifice of accuracy. The performance of the cascade model is evaluated in terms of the trade-off between the classification accuracy and rejection rate, and the results confirm its effectiveness.