Çelik Mayadağlı, TubaSaatçı, ErtuğrulRifat, Edizkan2018-07-232018-07-2320171303-0914https://hdl.handle.net/11413/2278This 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.en-USFingerprintCellular Neural NetworksRotation InvariantFingerprint Recognition SystemCellular Neural-NetworksTimeArchitectureEmulatorSpaceArticle4110671000214110671000212-s2.0-850279602572-s2.0-85027960257