Dynamic handwritten Chinese signature verification

A dynamic handwritten Chinese signature verification system based upon a Bayesian neural network is presented. Due to a great deal of variability of handwritten Chinese signatures, the proposed Bayesian neural network is trained by an incremental learning vector quantization (ILVQ) algorithm, which endows this system with incremental learning ability, and outputs a posteriori probability to give a more reliable distance estimation. The performance analysis was based upon a set of signature data consisting of 800 true specimens, 200 simple forgeries and 200 skilled forgeries. The experimental results show the type I error is about 2% and the type II error rates are about 0.1% and 2.5% for simple and skilled forgeries, respectively