Input Fingerprint Images Research Articles

An efficient fingerprint verification system using integrated wavelet and Fourier–Mellin invariant transform

Today, minutiae-based and image-based are the two major approaches for the purpose of fingerprint authentication. Image based approach offers much higher computation efficiency with minimum pre-processing and proves also effective even when the image quality is too low to allow a reliable minutia extraction. However, this approach is vulnerable to shape distortions as well as variation in position, scale and orientation angle. In this paper, a novel method of image based fingerprint matching based on the features extracted from the integrated Wavelet and the Fourier–Mellin Transform (WFMT) framework is proposed to remedy these problems. Wavelet transform, with its energy compacted feature is used to preserve the local edges and reduce noise in the low frequency domain after image decomposition, and hence making the fingerprint images less sensitive to shape distortion. The Fourier–Mellin transform (FMT) served to produce a translation, rotation and scale invariant feature. Multiple WFMT features can be used to form a reference invariant feature through the linearity property of FMT and hence reduce the variability of the input fingerprint images. Based on this integrated framework, a fingerprint verification system is designed. The experiments show the verification accuracy is 5.66 and 1.01% of equal error rate is achieved when multiple WFMT features are used.

An effective algorithm for fingerprint image enhancement based on wavelet transform

Among all the fingerprint identification/verification systems, such as minutiae-based or filterbank-based fingerprint matching, the performance relies heavily on the quality of the input fingerprint images. In this paper, we propose an effective algorithm of fingerprint image enhancement, which can much improve the clarity and continuity of ridge structures based on the multiresolution analysis of global texture and local orientation by the wavelet transform. Experimental results show that the enhanced image quality by using the wavelet-based enhancement algorithm is much better than the other existing methods for improving the minutiae detection.

Fingerprint image enhancement: algorithm and performance evaluation

In order to ensure that the performance of an automatic fingerprint identification/verification system will be robust with respect to the quality of input fingerprint images, it is essential to incorporate a fingerprint enhancement algorithm in the minutiae extraction module. We present a fast fingerprint enhancement algorithm, which can adaptively improve the clarity of ridge and valley structures of input fingerprint images based on the estimated local ridge orientation and frequency. We have evaluated the performance of the image enhancement algorithm using the goodness index of the extracted minutiae and the accuracy of an online fingerprint verification system. Experimental results show that incorporating the enhancement algorithm improves both the goodness index and the verification accuracy.

On-line fingerprint verification

Fingerprint verification is one of the most reliable personal identification methods. However, manual fingerprint verification is incapable of meeting today's increasing performance requirements. An automatic fingerprint identification system (AFIS) is needed. This paper describes the design and implementation of an online fingerprint verification system which operates in two stages: minutia extraction and minutia matching. An improved version of the minutia extraction algorithm proposed by Ratha et al. (1995), which is much faster and more reliable, is implemented for extracting features from an input fingerprint image captured with an online inkless scanner. For minutia matching, an alignment-based elastic matching algorithm has been developed. This algorithm is capable of finding the correspondences between minutiae in the input image and the stored template without resorting to exhaustive search and has the ability of adaptively compensating for the nonlinear deformations and inexact pose transformations between fingerprints. The system has been tested on two sets of fingerprint images captured with inkless scanners. The verification accuracy is found to be acceptable. Typically, a complete fingerprint verification procedure takes, on an average, about eight seconds on a SPARC 20 workstation. These experimental results show that our system meets the response time requirements of online verification with high accuracy.

Automatic Fingerprint Identification

The principle of fingerprint (FP) identification and a Pattern Recognition Model for automatic fingerprint identification is given. The input fingerprint image is subjected to a number of preprocessing operations to improve the quality of the image. Contextual filters suitable for fingerprint image enhancement are developed. Direction based segmentation is used to separate the foreground from the background and noisy areas. The ridge endings and bifurcation points, the minutiae, are the features used for FP identification. The algorithms for various preprocessing operations and feature extraction together with the approaches for FP matching are discussed. These algorithms have been tested for more than 10,000 FP imges of different quality. The results are found to be good and can be employed in practical applications.