A CNN based rotation invariant fingerprint recognition system - Çelik Mayadağlı, Tuba; Saatçı, Ertuğrul; Rifat, Edizkan (Istanbul Unıv, Fac Engineering, Elektrik-Elektronik Mühendisliği Bölümü, Avcılar Kampüsü, İstanbul, 34320, Turkey, 2017)
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.
Keywords: Fingerprint; Cellular Neural Networks; Rotation Invariant; Fingerprint Recognition System; Cellular Neural-Networks; Time; Architecture; Emulator; Space