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 problems such as scaling, rotation, translation and varying illumination conditions. SIFT feature had a capability to withstand 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 in optimum condition. That was a better result than only use SIFT or LEBP feature.

Original languageEnglish
Pages (from-to)478-485
Number of pages8
JournalTelkomnika (Telecommunication Computing Electronics and Control)
Issue number1
Publication statusPublished - Mar 2017


  • Finger vein
  • Learning vector quantization
  • Local extensive binary pattern
  • Multi-feature fusion
  • Scale invariant feature transform


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