Signature Verification using a Monte Carlo-based Updating Algorithm Adapted to Intersession Variability

A factor known as intersession variability in signatures causes deterioration of authentication performance. We propose a novel algorithm that includes a model updating scheme to correct for this variability. A model was provided for each user to calculate a score using fused multiple distance measures with respect to previous work. The algorithm consisted of an updating phase in addition to a training phase and a testing phase. In the training phase, the model's parameters were sampled using a Markov chain Monte Carlo method for each individual. In the testing phase, the generated model was used to determine whether a test signature was genuine. In the updating phase, the parameters were updated with test data using a sequential Monte Carlo (SMC) algorithm. Adoption of a parameter for automatically adjusting a hyper parameter in SMC improved the authentication performance. Several experiments were performed on signatures from a public database. The proposed algorithm achieved an EER of 7.59%