Reconstruction of fingerprints from minutiae points

Most fingerprint authentication systems utilize minutiae information to compare fingerprint images. During enrollment, the minutiae template of a user\u27s fingerprint is extracted and stored in the database. In this work, we concern ourselves with the amount of fingerprint information that can be elicited from the minutiae template of a user\u27s fingerprint. We demonstrate that minutiae information can reveal substantial details such as the orientation field and class of the (unseen) parent fingerprint that can potentially be used to reconstruct the original fingerprint image.;Given a minutiae template, the proposed method first estimates the orientation map of the parent fingerprint by constructing minutiae triplets. The estimated orientation map is observed to be remarkably consistent with the underlying ridge flow of the unseen parent fingerprint. We also discuss a fingerprint classification technique that utilizes only the minutiae information to determine the class of the fingerprint (Arch, Left loop, Right loop and Whorl). The proposed classifier utilizes various properties of the minutiae distribution such as angular histograms, density, relationship between minutiae pairs, etc. A classification accuracy of 82% is obtained on a subset of the NIST-4 database. This indicates that the seemingly random minutiae distribution of a fingerprint can reveal important class information. (Abstract shortened by UMI.)

Deep Fingerprint Matching from Contactless to Contact Fingerprints for Increased Interoperability

Contactless fingerprint matching is a common form of biometric security today. Most smartphones and associated apps now let users opt into using this form of biometric security. However, it’s difficult to match a finger-photo to a fingerprint because of perspective distortion occurring at the edges of the finger-photo, so direct matching using conventional methods will not be as accurate due to a lack of sufficient matching minutiae points. To address this issue, we propose a deep model, Perspective Distortion Rectification Model (PDRM), to estimate the fingerprint correspondence for finger-photo images in order to recover more minutiae points. Not only do we determine the feasibility of matching synthesized fingerprints from finger-photos, but we also show that matching a finger-photo to a fingerprint directly is possible by using our proposed Coupled Generative Adversarial Network (CpGAN) verifier. The results from our PDRM show that our method for creating synthetic fingerprints from finger-photos provides a more accurate matching (AUC=96.4%, EER= 8.9%) than just using the same commercial matcher to match finger-photo and fingerprints directly (AUC=92.1%, EER=15.7%). Finally, our proposed CpGAN verifier provides the best matching accuracy with AUC=98.4% and EER=6.3%

Enhancement of Latent Fingerprint Recognition Using Global Transform

Latent Fingerprints plays a vital role in identifying thefts, crime etc. Latent fingerprints are of 3 types. Noise in the Latent Fingerprints is removed by smoothing. Manual marking in Latent Fingerprint is slow and also latent examiner may make mistake while marking. The minutiae in the same latent marked by different latent examiners or even by the same examiner (but at different times) may not be the same. To overcome this issue new Orientation field estimation algorithm is introduced. It based on latent fingerprint feature extraction and edge detection. Orientation field estimation algorithm has dictionary construction stage. Dictionary Construction has 2 Stages. i) Offline stage ii) online stage. Orientation field estimation algorithm is applied for Overlapped fingerprint. Hough transform is used for detecting edges. It is shown that this method is slower to recognize latent fingerprint feature extraction and edge linking. In order to further increase the speed and perfect edge linking Hough transform method can be modified for better performance. Global transform is used for perfect edge linking and get the full fingerprint structure and comparison is made between two transforms to show which transform is better. DOI: 10.17762/ijritcc2321-8169.15034