Open AccessCombination a Skeleton Filter and Reduction Dimension of Kernel PCA Based on Palmprint Recognition
Author(s) -
Muhammad Kusban,
Adhi Susanto,
Oyas Wahyunggoro
Publication year - 2016
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v6i6.pp3255-3261
Subject(s) - artificial intelligence , pattern recognition (psychology) , computer science , biometrics , dimension (graph theory) , reduction (mathematics) , kernel (algebra) , filter (signal processing) , identification (biology) , feature (linguistics) , dimensionality reduction , computer vision , face (sociological concept) , process (computing) , palm print , mathematics , social science , linguistics , philosophy , botany , geometry , combinatorics , sociology , pure mathematics , biology , operating system
Palmprint identification is part of biometric recognition, which attracted many researchers, especially when fusion with face identification that will be applied in the airport to hasten knowing individual identity. To accelerate the process of verification feature palms, dimension reduction method is the dominant technique to extract the feature information of palms.The mechanism will boost if the ROI images are processed prior to get normalize image enhancement.In this paper with three sample input database, a kernel PCA method used as a dimension reduction compared with three others and a skeleton filter used as a image enhancement method compared with six others. The final results show that the proposed method successfully achieve the target in terms of the processing time of $ 0.7415 $ second, the EER performance rate of 0.19 % and the success of verification process about 99,82 %.