Fingerprint classification based on graylevel fuzzy clustering co-occurrence matrix

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Atıf İçin Kopyala

Topaloglu N.

ENERGY EDUCATION SCIENCE AND TECHNOLOGY PART A-ENERGY SCIENCE AND RESEARCH, cilt.29, sa.2, ss.1125-1132, 2012 (SCI-Expanded)

• Yayın Türü: Makale / Tam Makale
• Cilt numarası: 29 Sayı: 2
• Basım Tarihi: 2012
• Dergi Adı: ENERGY EDUCATION SCIENCE AND TECHNOLOGY PART A-ENERGY SCIENCE AND RESEARCH
• Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED)
• Sayfa Sayıları: ss.1125-1132
• Gazi Üniversitesi Adresli: Hayır

Özet

Fingerprint classification is a very important step in the fingerprint matching process in particular when the database is large, since it can provide an indexing mechanism. In this paper a finger print classification based on the Gray-Level Fuzzy Clustering Co-Occurrence Matrix is proposed. It is based on the fact that a fingerprint image is composed of regular texture regions that can be successfully represented by Gray-Level Fuzzy clustering co-occurrence matrices. So, we first extract the features based on certain characteristics of the Fuzzy Clustering co-occurrence matrix and then we use these features to train a neural network for classifying fingerprints into six common classes. The obtained results compared with the existing approaches demonstrate the superior performance of our proposed approach.