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A novel approach for feature extraction of fingerprint matching is proposed by using two-dimensional (2D) rotated wavelet filters (RWF). 2D RWF are used to capture the characterization of diagonally oriented information present in fingerprint image. Proposed method extracts the significant information from small area of fingerprint image. Experimental results conducted on standard database of Bologna University and FVC2002 indicate that the proposed method improves the genuine acceptance rate (GAR) from 92.14% to 96.12% and reduces false acceptance rate (FAR) from 25.2% to 21.2% on Bologna University database and it reduces FAR from 36.71% to 22.79% on FVC2002 database compared with discrete wavelet transform-based approach.

Conventional algorithms for fingerprint recognition are mainly based on minutiae information. However, the small number of minutiae in partial fingerprints is still a challenge in fingerprint matching. In this paper, a novel algorithm is proposed to improve the performance of partial fingerprint matching. A simulation scheme was firstly proposed to construct a serial of partial fingerprints with different area. Then, the influence of the fingerprint area in partial fingerprint recognition is studied. By comparing the performance of partial fingerprint recognition with different fingerprint area, some useful conclusions can be drawn: (1) The decrease of the fingerprint area degrades the performance of partial fingerprint recognition; (2) When the fingerprint area decreases, the genuine matching scores will decrease, whereas the imposter matching scores will increase. Based on these observations, we proposed a fusion scheme based on modified support vector machine (SVM) to combine the area information for fingerprint recognition. Experimental result illustrates the effectiveness of the proposed method.

We present the design and implementation of a fingerprint-based remote authentication system. In this system, the fingerprint features captured at a client terminal are transmitted over a communication channel to a central server location where the verification takes place. In order to overcome replay attacks, we propose an original approach to fingerprint matching that relies on different fingerprint features for every execution of the authentication protocol. The paper also introduces an efficient approach for fingerprint image enhancement that constitutes the core of the feature extraction module included in the proposed authentication system. A public domain collection of fingerprint images is used to evaluate the system performance. The experimental results reveal that the proposed system can achieve good matching performance on this data collection and, furthermore, is able to accurately reject replay attacks.

Many Automatic Fingerprint Identification Systems (AFIS) are based on minutiae matching. Minutiae are the terminations and bifurcations of the ridge lines in a fingerprint image. A gray-scale fingerprint image that has undergone binarization, followed by thinning, in order to extract the minutiae, may contain hundreds of minutiae, all of which are not so vivid and obvious in the original image. Thus, the set of minutiae that are well-defined and more prominent than the rest should be given higher relevance and importance in the process of minutiae matching.

In this work, a gray-scale fingerprint image is first preprocessed to produce a thinned binary image. Next, a method to assign a score value to each of the extracted minutiae is proposed, based on certain topographical properties of a minutia. The score associated to a minutia signifies its robustness and prominence. A minutia with a higher score value should be considered with higher priority in the matching scheme to yield better results. Experimental results on several standard databases have been reported.

A minutiae relationship representation and matching method based on curve coordinate system is proposed. For each minutia, a curve coordinate system is established, and the coordinates of other minutiae in this coordinate system is computed. Thus, the coordinate relationship between each pair of minutiae can be evaluated. These relationships are used for pairing minutiae between the template fingerprint and the query fingerprint by means of transferring reference minutiae. The algorithm is tested on FVC2004DBs which include many highly distorted fingerprints. Results have shown that the proposed algorithm achieves improved matching accuracy and is able to cope with highly distorted fingerprints.

Most of the documents use fingerprint impression for authentication. Property related documents, bank checks, application forms, etc., are the examples of such documents. Fingerprint-based document image retrieval system aims to provide a solution for searching and browsing of such digitized documents. The major challenges in implementing fingerprint-based document image retrieval are an efficient method for fingerprint detection and an effective feature extraction method. In this work, we propose a method for automatic detection of a fingerprint from given query document image employing Discrete Wavelet Transform (DWT)-based features and SVM classifier. In this paper, we also propose and investigate two feature extraction schemes, DWT and Stationary Wavelet Transform (SWT)-based Local Binary Pattern (LBP) features for fingerprint-based document image retrieval. The standardized Euclidean distance is employed for matching and ranking of the documents. Proposed method is tested on a database of 1200 document images and is also compared with current state-of-art. The proposed scheme provided 98.87% of detection accuracy and 73.08% of Mean Average Precision (MAP) for document image retrieval.

Fingerprint matching is a key technique of automatic fingerprint recognition system. The result of point pattern matching is accurate while its repeated search for maximum likelihood pairs are very time-consuming. In this paper, we proposed a fast complex fingerprint matching method to solve this problem. The innovative method constructs rotation-and-translation-invariant feature vectors to describe local information of minutiae sets and shifts the matching style from point pattern to feature vector. If the decision is uncertain by this case, then it estimates rotation and translation parameters from previous matched pairs and returns to point matching. Since this method takes the similarity and consistency of local deformation into account, it is not only much faster, but also lower false accepted than traditional approaches. The test on FVC2004 data sets shows that the equal error rate (EER) is 2.97%, and the mean matching time is 10.3 milliseconds, variance is 2.03, superior to most traditional methods. When minutiae number is among 15-40, the EER reaches to 2.14%.

Fingerprint matching is an important problem in fingerprint identification. In this paper, we propose a novel minutiae-orientation vector(MOV) for fingerprint matching. MOV combines both orientation information and neighborhood minutiae information. A set of reference point pairs is identified based on MOV. Alignment is done coordinately and directionally based on these reference point pairs. Matching score is computed. The experimental results show that the proposed matching scheme is very competitive as compared with those algorithms, which have been participated in FVC2004 DB2_A.