Open AccessPenentuan Klas Sidik Jari Berdasarkan Arah Kemiringan Ridge
Author(s) -
Sri Suwarno,
Agus Harjoko
Publication year - 2011
Publication title -
indonesian journal of computing and cybernetics systems
Language(s) - English
Resource type - Journals
eISSN - 2460-7258
pISSN - 1978-1520
DOI - 10.22146/ijccs.5208
Subject(s) - ridge , learning vector quantization , preprocessor , block (permutation group theory) , pattern recognition (psychology) , fingerprint (computing) , artificial intelligence , computer science , feature (linguistics) , mathematics , vector quantization , geography , cartography , geometry , linguistics , philosophy
Researches on fingerprint classification are generally based on its features such as core and delta. Extraction of these features are generally preceded by a variety of preprocessing. In this study the classification is done directly on the fingerprint image without preprocessing. Feature used as the basis for classification is the direction of the ridge. The direction of the ridge is determined by the slope of the blocks that are exist on every ridge. Fingerprint image is divided into blocks of size 3x3 pixels and the direction of each block is determined. Direction of the slope of the block are grouped into 8, these are north, north-east, east, south-east, south, south-west, west and north-west. The number of blocks in each direction form the basis of classification using Learning Vector Quantization network (LVQ). This study used 80 data samples from the database of FVC2004. This model obtained classification accuracy of up to 86.3%. Keywords—fingerprint, classification, ridge, LVQ