Global features selection for dynamic signature verification

In this paper we collect 1210 global features of dynamics signature. All features are collected from previous research and we also add global features from FRESH framework. The purpose is to select relevant features from those features set. For doing that, we compute the importance score of each features using two methods: Information Gain Ratio and Correlation. Then each features will be ranked based on its score. After that, we divide 1210 features into 10, 20, 30, and so on until all 1210 features set are used in ascending order of the rank. Then, each features set is validated with Random Forrest, SVM, and Naïve Bayes classifier using 10 folds cross validation. The experiment was conducted in SVC2004 dataset task 1. The result show that, 120 first features from correlation method is the most relevant features. And therefore, those 120 global features is a good candidate for dynamic signature verification and will be used for the next research using dataset collected from mobile device.