Fingerprint Image Processing for Automatic Verification (original) (raw)
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An algorithm for fingerprint image postprocessing
2000
Most of the current fingerprint identification and verification systems perform fingerprint matching based on different attributes of the minutia details present in fingerprints. The minutiae (i.e. ridge endings and ridge bifurcations) are usually detected in the thinned binary image of the fingerprint. Due to the presence of noise as well as the use of different preprocessing stages the thinned binary image contains a large number of false minutiae which may highly decrease the matching performances of the system. A new algorithm of fingerprint image postprocessing is proposed in this paper The algorithm operates onto the thinned binary image of the fingerprint, in order to eliminate the false minutiae. The proposed algorithm is able to detect and cancel the minutiae associated with most of the false minutia structures which may be encountered in the thinned fingerprint image.
ADAPTIVE FINGERPRINT IMAGE ENHANCEMENT USING PROCESSING BLOCKS
IJRCAR, 2014
Adaptive fingerprint enhancement method is based on contextual filtering. Contextual filtering is a popular technique for fingerprint enhancement, where topological filter features are aligned with the local orientation and frequency of the ridges in the fingerprint image. The term adaptive implies that parameters of the method are automatically adjusted based on the input fingerprint image. Fingerprint image enhancement is mainly used in ridge structure. Ridge structures in fingerprint images are not always well defined; therefore, enhancement algorithm is necessary .Two important ridge characteristics: Ridge ending, Ridge bifurcation. The adaptive fingerprint enhancement method comprises five processing blocks. 1) Pre-processing; 2) global analysis; 3) local analysis; and 4) matched filtering; 4) Image segmentation. In the pre-processing and local analysis blocks, a nonlinear dynamic range adjustment method, SMQT is used. In the global analysis and matched filtering blocks, different forms of order statistical filters are applied. These processing blocks yield an improved and new adaptive fingerprint image processing method.
Fingerprint Image Enhancement Based on Various Techniques, Feature Extraction and Matching
Exact involuntary individual recognition is critical in a diversity of applications in our electronically organized society. Biometrics, which mentions to recognition based on physical or behavioral characteristics, is being increasingly adopted to give positive recognition with a high degree of confidence. Among all biometric techniques, fingerprint-based authentication schemes have established most attention because of long history of fingerprints and their general apply in forensics. Fingerprints are a great source for recognition of individuals. Fingerprint recognition is one of the forms of biometric recognition. However obtaining a decent fingerprint image is not always easy. So, fingerprint image should be pre-processed by matching. The main objective of this work is to propose an image matching algorithm which is useful to every image for matching. For professional enhancement and feature extraction procedures, the segmented structures should be invalid of every noise. A pre-processing method containing of field course, ridge frequency estimated, filtering, partition and enhancement is performed. The attained image is useful to a thinning algorithm and following minutiae removal. The association of image pre-processing and minutiae extraction is deliberated. The simulations are performed in the MATLAB atmosphere to estimate the performance of the implemented algorithms. MATLAB provides a valuable atmosphere for these progresses. Outcome and interpretation of the fingerprint images
Image enhancement and minutiae matching in fingerprint verification
Pattern Recognition Letters, 2003
Fingerprint image enhancement and minutiae matching are two key steps in an automatic fingerprint identification system. In this paper, we develop a fingerprint image enhancement algorithm based on orientation fields; According to the principles of Jain et al.Õs matching algorithm, we also introduce ideas along the following three aspects: introduction of ridge information into the minutiae matching process in a simple but effective way, which solves the problem of reference point pair selection with low computational cost; use of a variable sized bounding box to make our algorithm more robust to non-linear deformation between fingerprint images; use of a simpler alignment method in our algorithm. Experiments using the Fingerprint Verification Competition 2000 (FVC2000) databases with the FVC2000 performance evaluation show that these ideas are effective.
Fingerprint image enhancement using filtering techniques
2005
E xtracting minutiae from fingerprint images is one of the most important steps in automatic fingerprint identification and classification. Minutiae are local discontinuities in the fingerprint pattern, mainly terminations and bifurcations. Most of the minutiae detection methods are based on image binarization while some others extract the minutiae directly from gray-scale images. In this work we compare these two approaches and propose two different methods for fingerprint ridge image enhancement. The first one is carried out using local histogram equalization, Wiener filtering, and image binarization. The second method uses a unique anisotropic filter for direct gray-scale enhancement. The results achieved are compared with those obtained through some other methods. Both methods show some improvement in the minutiae detection process in terms of time required and efficiency.
Fingerprint image enhancement: Algorithm and performance evaluation
Pattern Analysis and Machine …, 1998
A critical step in automatic ngerprint matching is to automatically and reliably extract minutiae from the input ngerprint images. However, the performance of a minutiae extraction algorithm relies heavily on the quality of the input ngerprint images. In order to ensure that the performance of an automatic ngerprint identi cation/veri cation system will be robust with respect to the quality of input ngerprint images, it is essential to incorporate a ngerprint enhancement algorithm in the minutiae extraction module. We present a fast ngerprint enhancement algorithm, which can adaptively improve the clarity of ridge and furrow structures of input ngerprint 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 ngerprint veri cation system. Experimental results show that incorporating the enhancement algorithm improves both the goodness index and the veri cation accuracy.
—This article proposes several improvements to an adaptive fingerprint enhancement method that is based on contextual filtering. The term adaptive implies that parameters of the method are automatically adjusted based on the input fingerprint image. Five processing blocks comprise the adaptive fingerprint enhancement method, where four of these blocks are updated in our proposed system. Hence, the proposed overall system is novel. The four updated processing blocks are; pre-processing, global analysis, local analysis and matched filtering. In the pre-processing and local analysis blocks, a nonlinear dynamic range adjustment method is used. In the global analysis and matched filtering blocks, different forms of order statistical filters are applied. These processing blocks yield an improved and new adaptive fingerprint image processing method. The performance of the updated processing blocks is presented in the evaluation part of this paper. The algorithm is evaluated towards the NIST developed NBIS software for fingerprint recognition on FVC databases.
Fingerprint Image Enhancement and Extraction of Minutiae and Orientation
Fingerprints are popular among the biometric – based systems due to ease of acquisition, uniqueness and availability. Fingerprint based biometric systems work by extracting and matching some features on the fingerprint. Due to errors in acquisition phase, it is possible that the scanned fingerprint image is not of a good quality and hence needs to be enhanced before being processed by the feature extracting module. Out of the various features that can be extracted, orientation and minutiae points are the most common ones to be used. This paper discusses some commonly used fingerprint enhancement techniques, the algorithms for minutiae and orientation extraction followed by the comparison of the algorithm on various databases.
Improved Technique for Fingerprint Segmentation
a valley is the region between two adjacent ridges. The minutiae, which are the local discontinuities in the ridge flow pattern, provide the features that are used for identification. Details such as the type, orientation, and location of minutiae are taken into account when performing minutiae extraction . Galton [5] defined a set of features for fingerprint identification, which since then, has been refined to include additional types of fingerprint features. How-ever, most of these features are not commonly used in fingerprint identification systems. Instead the set of minutiae types are restricted into only two types, ridge endings and bifurcations, as other types of minutiae can be expressed in terms of these two feature types. Ridge endings are the points where the ridge curve terminates, and bifurcations are where a ridge splits from a single path to two paths at a Y-junction. occur due to variations in skin and impression conditions such as scars, humidity, dirt, and non-uniform contact with the fingerprint capture device . Thus, image enhancement techniques are often employed to reduce the noise and enhance the definition of ridges against valleys.
Biometrics is the science and technology of measuring and analyzing biological data of human body, extracting a feature set from the acquired data, and comparing this set against to the template set in the database. Biometric techniques are gaining importance for personal authentication and identification as compared to the traditional authentication methods. Biometric templates are vulnerable to variety of attacks due to their inherent nature. When a person's biometric is compromised his identity is lost. In contrast to password, biometric is not revocable.