Éditeur(s) :
HAL CCSD Résumé : International audience
We present a novel approach towards signature recognition from spatio-temporal data. The data is obtained by recording gyroscope and accelerometer measurements from an embedded pen device. The idea of Universum learning was previously presented by Vapnik and recently popularized in machine learning community. It assumes that the decision boundary of a classifier lies close to data with high uncertainty. The quality of the final classifier strongly depends on a way how to choose the Universum data and also on the representation of original data. In our paper we use a novel approach of Universum learning to classify signature data, also we present our novel idea how to sample the Universum data. At last, we also find more effective representation of the signature data itself compared to the baseline method. These three novelties allow us to outperform previously published results by 4.89% / 5.58%.
17th Biennial Conference of the International Graphonomics Society
Pointe-à-Pitre, Guadeloupe
hal-01165925
https://hal.univ-antilles.fr/hal-01165925 https://hal.univ-antilles.fr/hal-01165925/document https://hal.univ-antilles.fr/hal-01165925/file/IGS_2015_submission_38.pdf