Fingerprint matching from minutiae texture maps

with identity fraud in our society reaching unprecedented proportions and with an increasing emphasis on the emerging automatic personal identification applications, biometrics-based verification, especially fingerprint-based identification, is receiving a lot of attention. There are two major shortcomings of the traditional approaches to fingerprint representation. For a considerable fraction of population, the representations based on explicit detection of complete ridge structures in the fingerprint are difficult to extract automatically. The widely used minutiae-based representation does not utilize a significant component of the rich discriminatory information available in the fingerprints. Local ridge structures cannot be completely characterized by minutiae. Further, minutiae-based matching has difficulty in quickly matching two fingerprint images containing different number of unregistered minutiae points. The proposed filterbased algorithm uses a bank of Gabor filters to capture both local and global details in a fingerprint as a compact fixed length Finger Code. The fingerprint matching is based on the Euclidean distance between the two corresponding Finger Codes and hence is extremely fast.

Minutiae-based latent fingerprint matching method is one of the supreme current approaches in fingerprint matching. Due to its high performance, less time consumption and high quality image it is used in business applications. A latent fingerprint contains an outline of ridges and valleys on the plane of a fingertip. The ridges have endpoints and crossing points which are called as minutiae. The minutiae pattern which is present in every finger is fixed and stable. And this minutia is used in fingerprint matching process. In minutiae matching method, it aligns the minutiae present in the input image and stored that in templates after that it finds the number of minutiae matched. Generally, a minutiae-based matching algorithm has to resolve two problems which occurred during fingerprint matching that are correspondence and similarity computation. To solve the correspondence problem use two descriptor such as texture based and minutiae based descriptor. And a greedy algorithm is also used to provide a similarity among the minutiae present in fingerprints. A 17-D feature vector is computed from the matching result and converted to a matching score by using a support vector classifier. The proposed algorithm was tested on local databases and compares it with all the participators in local database in the proposed algorithm.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003

We introduce a novel fingerprint representation scheme that relies on describing the orientation field of the fingerprint pattern with respect to each minutia detail. This representation allows the derivation of a similarity function between minutiae that is used to identify corresponding features and evaluate the resemblance between two fingerprint impressions. A fingerprint matching algorithm, based on the proposed representation, is developed and tested with a series of experiments conducted on two public domain collections of fingerprint images. The results reveal that our method can achieve good performance on these data collections and that it outperforms other alternative approaches implemented for comparison.

2001

Abstract The advent of solid-state fingerprint sensors presents a fresh challenge to traditional fingerprint matching algorithms. These sensors provide a small contact area (≈ 0.6"× 0.6") for the fingertip and, therefore, sense only a limited portion of the fingerprint. Thus multiple impressions of the same fingerprint may have only a small region of overlap.

International Conference on Pattern Recognition, 2004

Minutia matching is the most popular approach to fingerprint recognition. In this paper, we analyzed a novel fingerprint feature named adjacent orientation vector, or AOV, for fingerprint matching. In the first stage, AOV is used to find possible minutiae pairs. Then one minutiae set is rotated and translated. This is followed by a preliminary matching to ensure reliability as well

2008

We propose a new minutiae-based approach to match fingerprint images using similar structures. Distortion poses serious threats through altered geometry, increases false minutiae, and hence makes it very difficult to find a perfect match. This algorithm divides fingerprint images into two concentric circular regions -inner and outer -based on the degree of distortion. The algorithm assigns weightages for a minutiae-pair match based on the region in which the pair exists. The implementation of the algorithm on the standard FVC DB shows robust performance.

2019 International Conference on Frontiers of Information Technology (FIT)

Minutia Cylinder Codes (MCC) are minutiae based fingerprint descriptors that take into account minutiae information in a fingerprint image for fingerprint matching. In this paper, we present a modification to the underlying information of the MCC descriptor and show that using different features, the accuracy of matching is highly affected by such changes. MCC originally being a minutia only descriptor is transformed into a texture descriptor. The transformation is from minutiae angular information to orientation, frequency and energy information using Short Time Fourier Transform (STFT) analysis. The minutia cylinder codes are converted to minutiae texture cylinder codes (MTCC). Based on a fixed set of parameters, the proposed changes to MCC show improved performance on FVC 2002 and 2004 data sets and surpass the traditional MCC performance.

2021

The occurrences of finger-prints distortions would be predicted as the primary cause of effects for the false-mismatch of the fingerprints. This complication have the impact in all applications of finger-print recognition, and it would cause the negative recognition process and the deduplication process. In the group of such applications, the malicious-users would purposefully involve in distorting the fingerprints to collapse the identification mechanism. The novel classification algorithms is employed to detect the finger-print recognitions and to involve in the rectification of fingerprint-distortions on basis of single images of fingerprint. The features of the Minutiae points and the orientation-maps were fetched from the images of finger-prints. In the process of fingerprint-recognition, the distance measure is calculated on the basis of Euclidean-distance concept and the performance is assessed on the performance of classifier-type. In this methodology, the input finger-print...

2009 Second International …, 2009

In this paper, a new fingerprint verification algorithm is presented that improves matching accuracy by overcoming the shortcomings of previous methods due to poor image quality. It reduces multi-spectral noise by enhancing a fingerprint image to accurately and reliably determine a reference point using the orientation reliability and then extract a 129 X 129 block, making the reference point its center. From the 16 co-occurrence matrices, four statistical descriptors are computed. The experimental results have been analyzed using FVC testing protocol; the equal error rate (EER) is 0.32%. Furthermore, the comparison with other methods shows that the proposed method is more accurate and robust for reliable fingerprint verification.

IEEE Transactions on Image Processing, 2000

With identity fraud in our society reaching unprecedented proportions and with an increasing emphasis on the emerging automatic personal identification applications, biometrics-based verification, especially fingerprint-based identification, is receiving a lot of attention. There are two major shortcomings of the traditional approaches to fingerprint representation. For a considerable fraction of population, the representations based on explicit detection of complete ridge structures in the fingerprint are difficult to extract automatically. The widely used minutiae-based representation does not utilize a significant component of the rich discriminatory information available in the fingerprints. Local ridge structures cannot be completely characterized by minutiae. Further, minutiae-based matching has difficulty in quickly matching two fingerprint images containing different number of unregistered minutiae points. The proposed filter-based algorithm uses a bank of Gabor filters to capture both local and global details in a fingerprint as a compact fixed length FingerCode. The fingerprint matching is based on the Euclidean distance between the two corresponding FingerCodes and hence is extremely fast. We are able to achieve a verification accuracy which is only marginally inferior to the best results of minutiae-based algorithms published in the open literature [1]. Our system performs better than a state-of-the-art minutiae-based system when the performance requirement of the application system does not demand a very low false acceptance rate. Finally, we show that the matching performance can be improved by combining the decisions of the matchers based on complementary (minutiae-based and filter-based) fingerprint information.