A Digital Camera-Based Rotation-Invariant Fingerprint Verification Method

Abstract

Fingerprint registration and verification is an active area of research in the field of image processing. Usually, fingerprints are obtained from sensors; however, there is recent interest in using images of fingers obtained from digital cameras instead of scanners. An unaddressed issue in the processing of fingerprints extracted from digital images is the angle of the finger during image capture. To match a fingerprint with 100% accuracy, the angles of the matching features should be similar. This paper proposes a rotation and scale-invariant decision-making method for the intelligent registration and recognition of fingerprints. A digital image of a finger is taken as the input and compared with a reference image for derotation. Derotation is performed by applying binary segmentation on both images, followed by the application of speeded up robust feature (SURF) extraction and then feature matching. Potential inliers are extracted from matched features by applying the M-estimator. Matched inlier points are used to form a homography matrix, the difference in the rotation angles of the finger in both the input and reference images is calculated, and finally, derotation is performed. Input fingerprint features are extracted and compared or stored based on the decision support system required for the situation.

Convolutional neural network based children recognition system using contactless fingerprints

“…The proposed approach was tested using PolyU dataset [ 26 ] and contactless CLCF dataset and compared with open-source extractor MINDTCT from NBIS [ 25 ] and Contactless Net [ 11 ]. The comparative analysis is tabulated in Table 3 .…”

“…Yin et al proposed enhancement method for intrinsic image decomposition and guided image filtering [ 12 ]. Khan et al addressed rotation and scale-invariant images acquired through smartphones [ 11 ]. Sagiroglu et al proposed an apparatus for fingerprint identification using smartphones [ 9 ].…”

“…In this digital era, smartphones are helpful for acquiring images as they are well equipped with high resolution camera and high-speed processing power. Several studies have been conducted for identification of adults using fingerprints acquired in a contactless way [ 9 , 11 – 14 ]. Convolutional Neural Network (CNN) based approaches yield good results in contactless fingerprint identification [ 15 ].…”