A CNN Based Rotation Invariant Fingerprint Recognition System

Year 2017, Volume: 17 Issue: 2, 3471 - 3479, 27.07.2017

Tuba Celik Mayadaglı Ertugrul Saatcı , Rifat Edızkan

Abstract

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

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