Fingerprint recognition system to verify the identity of a person using an online database

Our B.Tech project emphasize on the current techniques for the fingerprint recognition. Human fingerprint exhibit some certain details marked on it. We categorized it as minutiae, which can be used as a unique identity of a person if we recognize it in a well manner. The main aim of this project is to design a complete system and an indigenous design model for fingerprint verification from an online database using minutiae matching technique. So, in order to have a good quality minutiae extraction the fingerprint image is first pre-processed by image enhancement which includes histogram equalization, Fast Fourier Transform and binerization and then segmentation is done to get the effective area of the fingerprint followed by minutiae extraction which includes ridge thinning and minutiae marking and then we have a post-processing operation which includes removal of H-breaks, isolated points and false minutiae. Now, we go for a final treatment which is ‘minutiae matching’, in minutiae matching we match the post-processed fingerprint image with the online database. For all these operations, we develop an alignment based matching algorithm which is for minutiae matching. This algorithm has a specialty that it itself finds the correspondences between input minutiae and the stored template minutiae pattern and there is no resorting to exhaustive search. We can then evaluate the performance of the system on a database by taking fingerprints of different people

A DoG based Approach for Fingerprint Image Enhancement

Fingerprints have been the most accepted tool for personal identification since many decades. It is also an invaluable tool for law enforcement and forensics for over a century, motivating the research in Automated fingerprint-based identification, an application of biometric system. The matching or identification accuracy using fingerprints has been shown to be very high. The theory on the uniqueness of fingerprint minutiae leads to the steps in studying the statistics of extracting the minutiae features reliably. Fingerprint images obtained through various sources are rarely of perfect quality. They may be degraded or noisy due to variations in skin or poor scanning technique or due to poor impression condition. Hence enhancement techniques are applied on fingerprint images prior to the minutiae point extraction to get sure of less spurious and more accurate minutiae points from the reliable minutiae location. This thesis focuses on fingerprint image enhancement techniques through histogram equalization applied locally on the degraded image. The proposed work is based on the Laplacian pyramid framework that decomposes the input image into a number of band-pass images to improve the local contrast, as well as the local edge information. The resultant image is passed through the regular methodologies of fingerprint, like ridge orientation, ridge frequency calculation, filtering, binarization and finally the morphological operation thinning. Experiments using different texture of images are conducted to enhance the images and to show a comparative result in terms of number of minutiae extracted from them along with the spurious and actual number existing in each enhanced image. Experimental results out performs well to overcome the counterpart of enhancement technique

Feature Level Fusion of Face and Fingerprint Biometrics

The aim of this paper is to study the fusion at feature extraction level for face and fingerprint biometrics. The proposed approach is based on the fusion of the two traits by extracting independent feature pointsets from the two modalities, and making the two pointsets compatible for concatenation. Moreover, to handle the problem of curse of dimensionality, the feature pointsets are properly reduced in dimension. Different feature reduction techniques are implemented, prior and after the feature pointsets fusion, and the results are duly recorded. The fused feature pointset for the database and the query face and fingerprint images are matched using techniques based on either the point pattern matching, or the Delaunay triangulation. Comparative experiments are conducted on chimeric and real databases, to assess the actual advantage of the fusion performed at the feature extraction level, in comparison to the matching score level.Comment: 6 pages, 7 figures, conferenc