An Intelligent System for Fingerprint Recognition and Verification Using Dilated Convolutional Neural Network and Extreme Learning Machine

Abstract

Several applications currently utilize fingerprint-based user recognition and authentication, but obtaining complete accuracy (eliminating false matches) is still challenging. Prior to feature extraction, improper image alignment was one of the causes of this problem. This paper proposes a novel fingerprint recognition and verification system using a dilated convolutional neural network (DICNN) and a weighted and bias-optimized extreme learning machine (WBELM). Image preprocessing, feature extraction, and fingerprint recognition are the main phases of the proposed work. To begin, image preprocessing is performed using bilateral filtering to suppress the noise in the gathered images. After that, the proposed system uses DICNN to extract features from the preprocessed images. From the extracted features, fingerprint recognition is done using WBELM. This study conducted experiments on the publicly available FVC 2004 dataset. The proposed model achieves 98.54 percent recognition accuracy with an elapsed time of 584 ms.