A CNN based rotation invariant fingerprint recognition system

Çelik Mayadağlı, Tuba
Saatçı, Ertuğrul
Rifat, Edizkan
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Istanbul Unıv, Fac Engineering, Elektrik-Elektronik Mühendisliği Bölümü, Avcılar Kampüsü, İstanbul, 34320, Turkey
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This paper presents a Cellular Neural Networks (CNN) based rotation invariant fingerprint recognition system by keeping the hardware implementability in mind. Core point was used as a reference point and detection of the core point was implemented in the CNN framework. Proposed system consists of four stages: preprocessing, feature extraction, false feature elimination and matching. Preprocessing enhances the input fingerprint image. Feature extraction creates rotation invariant features by using core point as a reference point. False feature elimination increases the system performance by removing the false minutiae points. Matching stage compares extracted features and creates a matching score. Recognition performance of the proposed system has been tested by using high resolution PolyU HRF DBII database. The results are very encouraging for implementing a CNN based fully automatic rotation invariant fingerprint recognition system.
Fingerprint, Cellular Neural Networks, Rotation Invariant, Fingerprint Recognition System, Cellular Neural-Networks, Time, Architecture, Emulator, Space