Study of Fingerprint Enhancement and Matching

Fingerprint is the oldest and popular form of bio-metric identification. Extract Minutiae is most used method for automatic fingerprint matching, every person fingerprint has some unique characteristics called minutiae. But studying the extract minutiae from the fingerprint images and matching it with database is depend on the image quality of finger impression. To make sure the performance of finger impression identification we have to robust the quality of fingerprint image by a suitable fingerprint enhancement algorithm. Here we work with a quick finger impression enhancement algorithm that improve the lucidity of valley and ridge structure based on estimated local orientation and frequency. After enhancement of sample fingerprint, sample fingerprint is matched with the database fingerprints, for that we had done feature extraction, minutiae representation and registration. But due to Spurious and missing minutiae the accuracy of fingerprint matching affected. We had done a detail relevant finger impression matching method build on the Shape Context descriptor, where the hybrid shape and orientation descriptor solve the problem. Hybrid shape descriptor filter out the unnatural minutia paring and ridge orientation descriptor improve the matching score. Matching score is generated and utilized for measuring the accuracy of execution of the proposed algorithm. Results demonstrated that the algorithm is exceptionally satisfactory for recognizing fingerprints acquired from diverse sources. Experimental results demonstrate enhancement algorithm also improves the matching accuracy

Accelerated Fingerprint Enhancement: A GPU-Optimized Mixed Architecture Approach

This document presents a preliminary approach to latent fingerprint enhancement, fundamentally designed around a mixed Unet architecture. It combines the capabilities of the Resnet-101 network and Unet encoder, aiming to form a potentially powerful composite. This combination, enhanced with attention mechanisms and forward skip connections, is intended to optimize the enhancement of ridge and minutiae features in fingerprints. One innovative element of this approach includes a novel Fingerprint Enhancement Gabor layer, specifically designed for GPU computations. This illustrates how modern computational resources might be harnessed to expedite enhancement. Given its potential functionality as either a CNN or Transformer layer, this Gabor layer could offer improved agility and processing speed to the system. However, it is important to note that this approach is still in the early stages of development and has not yet been fully validated through rigorous experiments. As such, it may require additional time and testing to establish its robustness and usability in the field of latent fingerprint enhancement. This includes improvements in processing speed, enhancement adaptability with distinct latent fingerprint types, and full validation in experimental approaches such as open-set (identification 1:N) and open-set validation, fingerprint quality evaluation, among others

A comparison of six fingerprint enhancement techniques for the recovery of latent fingerprints from unfired cartridge cases

This work compared the effectiveness of six different enhancement methods on six different sizes of brass cartridges. One sebaceous fingerprint was deposited onto twenty-five of each size of cartridge to enable a statistical evaluation of the enhancement methods for each cartridge size to be undertaken. The enhancement methods compared were superglue followed by BY40, superglue followed by gun blue followed by BY40, gun blue only, superglue followed by palladium deposition, palladium deposition only, and powder suspension. The six different cartridges used in this study were .22s, .32s, 9mm, .38s, ribbed shotgun, and smooth shotgun. The study found that more potentially identifiable fingerprints were enhanced on the larger cartridge cases. This was due to the surface area on the smaller cartridges, and in particular the .22s provided little ridge detail. Two techniques provided the best results - superglue followed by gun blue followed by BY40, and superglue followed by palladium deposition. This showed that the combination of the cyanoacrylate fuming and the metal oxidation reactions is increasing the yield of potentially identifiable fingerprints compared with the use of the techniques separately. Both techniques were also found to give reproducible results. These two enhancement techniques were also compared statistically and no statistical difference in their effectiveness was found suggesting both techniques are equally as effective at enhancing fingerprints on brass cartridge cases

Curved Gabor Filters for Fingerprint Image Enhancement

Gabor filters play an important role in many application areas for the enhancement of various types of images and the extraction of Gabor features. For the purpose of enhancing curved structures in noisy images, we introduce curved Gabor filters which locally adapt their shape to the direction of flow. These curved Gabor filters enable the choice of filter parameters which increase the smoothing power without creating artifacts in the enhanced image. In this paper, curved Gabor filters are applied to the curved ridge and valley structure of low-quality fingerprint images. First, we combine two orientation field estimation methods in order to obtain a more robust estimation for very noisy images. Next, curved regions are constructed by following the respective local orientation and they are used for estimating the local ridge frequency. Lastly, curved Gabor filters are defined based on curved regions and they are applied for the enhancement of low-quality fingerprint images. Experimental results on the FVC2004 databases show improvements of this approach in comparison to state-of-the-art enhancement methods