کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
388900 | 660946 | 2008 | 7 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Over-complete feature generation and feature selection for biometry Over-complete feature generation and feature selection for biometry](/preview/png/388900.png)
In this paper a novel method for obtaining an appropriate representation of patterns is presented. The information is extracted using an over-complete global feature combination, and then the most useful features are selected by sequential forward floating selection (SFFS).This new method has been tested in two problems: trained integration of iris and face biometrics; on-line signature verification system based on global information and a one-class classifier (Parzen Window Classifier).To the best of our knowledge, this is the first work that studies and proposes a set of “artificial” features for combining biometric matchers, created starting from the scores of the matchers. We show that a classifier trained on such set of features gains a noticeable performance improvement with respect to fixed fusion rules and other trained fusion methods.Moreover, we show that an on-line signature matcher based on the “artificial” features gains a noticeable performance improvement with respect to a matcher based on the “original” global features.
Journal: Expert Systems with Applications - Volume 35, Issue 4, November 2008, Pages 2049–2055