A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Effectiveness of pen pressure, azimuth, and altitude features for online signature verification
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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For Pen-input on-line signature verification algorithms, the influence of intersession variability is a considerable problem because hand-written signatures change with time, causing performance degradation. In our previous work, we proposed a user-generic model using AdaBoost. However, this model did not allow for the fact that features of signatures change over time. In this paper, we propose a template renewal method to reduce the performance degradation caused by signature changes over time. In our proposed method, the oldest template is replaced with a new one if the new signature data gives rise to an index which exceeds a threshold value. No further learning is necessary. A preliminary experiment was conducted on a subset of the MCYT database.