Biometric Recognition Based on Palm Vein Image Using Learning Vector Quantization
Herry Setiawan, Eko Mulyanto Yuniarno · 2017
Every human being has its own uniqueness among humans that other good physical form or characteristic trait. Biometrics is the science that can recognize a part of an individual. Therefore, biometric identification is one of the ways that is done to identify the identity of the person. Palm vein biometric is one who lately attracted many researchers and industry because it has several advantages over other physical characteristics such as fingerprint, iris and face. Palm vein has internal features making it difficult undermined, modified and simulated with fake palms. In this study, design and implementation will do a recognition system through the venous vessels. Systems that were built capable of taking images of the Palm veins, detect the presence of venous vessels are then able to tell based on the database. This is done after the process of extraction, processing and manufacturing characteristics against the image of Palm vein vessels. The method used is the phase and learning vector quantization symmetri. Feature extraction using phase symmetri. phase symmetri applied by calculating the amplitude and phase of the signal frequency of the image of the vein which represents detailed information detailed variant of an image of the vein. While learning vector quantization is used for grouping the exclusive result of extraction. This research resulted in the classification with accuracy reaching 94%.