Palmprint Image Dataset with Gabor Filter Feature Enchancement

Published: 23 May 2020| Version 2 | DOI: 10.17632/r8hxykxnk5.2
Darma Putra I Ketut Gede,
I Made Suwija Putra,
Putu Jhonarendra


The palmprint dataset is captured on left hand. Palmprint dataset is acquired from 15 people with 5 to 8 images of each person. To increase the amount of data in each person, the raw dataset was filtered with Gabor Filter. The characteristics of the Gabor Filter are good applied to palmprint image because the image has many variations of line direction and the thickness. The palmprint dataset has 20 to 32 images each class after applying the Gabor Filter. The author trains the palmprint dataset using the Convolutional Neural Network method.