Multi-feature Fusion Using SIFT and LEBP for Finger Vein Recognition
Abstract: In this paper,
multi-feature fusion using Scale Invariant Feature Transform (SIFT) and Local Extensive
Binary Pattern (LEBP) was proposed to obtain a feature that could resist
degradation problemssuch as scaling, rotation, translation and varying
illumination conditions. SIFT feature had a capability towithstand degradation
due to changes in the condition of the image scale, rotation and translation. Meanwhile,
LEBP feature had resistance to gray level variations with richer and
discriminatory local characteristics information. Therefore the fusion
technique is used to collect important information from SIFT and LEBP
feature.The resulting feature of multi-feature fusion using SIFT and LEBP
feature would be processed by Learning Vector Quantization (LVQ) method to
determine whether the testing image could be recognized or not. The accuracy
value could achieve 97.50%, TPR at 0.9400 and FPR at 0.0128 inn optimum
condition. That was a better result than only use SIFT or LEBP feature.
Keywords: finger vein, scale
invariant feature transform, local extensive binary pattern, multi-feature fusion,
learning vector quantization
Author: Hardika Khusnuliawati,
Chastine Fatichah, Rully Soelaiman
Journal Code: jptkomputergg170136
