A CNN based rotation invariant fingerprint recognition system

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.