Evaluating a New Conversive Hidden non-Markovian Model Approach for Online Movement Trajectory Verification

This paper presents further research on an implemented classification and verification system that employs a novel approach for stochastically modelling movement trajectories. The models are based on Conversive Hidden non-Markovian Models that are especially suited to mimic temporal dynamics of time series as in contrast to the relative Hidden Markov Models(HMM) and the dynamic time warping(DTW) method, timestamp information of data are an integral part. The system is able to create trajectory models from examples and is tested on signatures, doodles and pseudo-signatures for its verification performance. By using publicly available databases comparisons are made to evaluate the potential of the system. The results reveal that the system already performs similar to a general DTW approach on doodles and pseudo-signatures but does not reach the performance of specialized HMM systems for signatures. But further possibilities to improve the results are discussed.