Fingerprint Identification System using Tree Based Matching (original) (raw)

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.

Review on Fingerprint Recognition

—Biometric system works on behavioral and physiological biometric parameters to spot a person. Every fingerprint contains distinctive options and its recognizing system primarily works on native ridge feature local ridge endings, minutiae, core point, delta, etc. However, fingerprint pictures have poor quality thanks to variations in skin and impression conditions. In personal identification, fingerprint recognition is taken into account the foremost outstanding and reliable technique for matching with keep fingerprints within the information. Minutiae extraction is additional essential step in fingerprint matching. This paper provides plan regarding numerous feature extraction and matching algorithms for fingerprint recognition systems and to seek out that technique is additional reliable and secure. Keywords— fingerprint images, minutiae extraction, ridge endings, ridge bifurcation, fingerprint recognition.

A new approach to finger print matching technique

A fingerprint is a pattern of ridges and valleys that exist on the surface of the finger. The uniqueness of a fingerprint is typically, determined by the overall pattern of ridges and valleys as well as the local ridge anomalies e.g., a ridge bifurcation or a ridge ending, which are called minutiae points. Designing a reliable automatic fingerprint matching algorithm is quite challenging. However, the popularity of fingerprint sensors as they are becoming smaller and cheaper, automatic identification based on fingerprints is becoming not only attractive but an alternative complement to the traditional methods of identification. The critical factor in the widespread use of fingerprints identification is, satisfying the performance e.g., matching the speed and accuracy requirements of the application. The widely used minutiae-based representation utilizes this discriminatory information available in a fingerprint for a matching. However, we extend this process of matching through a tr...

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

Fingerprint Image Processing for Automatic Verification

1999

The performance of an automatic fingerprint verification approach relies heavily on the quality of the fingerprint image. Enhancement of the fingerprint image is then a crucial step in automatic fingerprint verification. This paper discusses the fingerprint image processing methods for automatic verification and proposes an adaptive oriented low pass filter to enhance the fingerprint image quality. For automatic fingerprint verification the fingerprint image processing is not aimed at improving the visual appearance of the fingerprint image but aimed at facilitating the subsequent processing. Therefore, the fingerprint image processing method is closely related to the method employed for the subsequent minutiae detection. The proposed approach takes efforts to increase the chances for success of the subsequent processes and at the same time avoid producing undesired side effects such as losing of the original ridge structure information or introducing additional spurious ridge structure information. Some sample results are given to illustrate the performance of the proposed approach.

Morphological enhancement and triangular matching for fingerprint recognition

2008

Among the principal problems for realizing a robust Automated Fingerprint Identification System (AFIS) there are the images quality and matching algorithms. In this paper a fingerprint enhancement algorithm based on morphological filter and a triangular matching are introduced. The enhancement phase is based on tree steps: directional decomposition, morphological filter and composition. For the matching phase a global transformation to overcame the effects of rotation, displacement and deformation between acquired and stored fingerprint is performed using the number of similar triangular, having fingerprint minutiae as vertexes. The performance of the proposed approach has been evaluated on two set of images: the first one is DB3 database from Fingerprint Verification Competition (FVC) and the second one is self collected using an optical scanner.

A Study on Fingerprint (biometrics) Recognition

Till now many algorithms are published for fingerprint recognition and these algorithms has different accuracy rate. This paper consists of information of about fingerprint (biometrics) recognition. The novel algorithm is considered for thinning process. Whole process of recognition is explained from image capturing to verification. The image captured is first converted to gray scale then image enrichment is done then thinning process take over charge which is main process then last process which is also equally important as thinning process is feature extraction which extracts ridge ending, bifurcation, and dot. The accuracy depends on the result of the three main process namely pre-processing, thinning process and feature extraction. Keywords: Arch, loop, whorl, Preprocessing, Thinning Process, Feature Extraction, Ridge. I. INTRODUCTION

Fingerprint Matching Through Minutiae Based Feature Extraction Method

2015

Here minutiae based feature extraction method has been discussed which is used for fingerprint matching. This method is mainly depending on the characteristics of minutiae of the individuals. The minutiae are ridge endings or bifurcations on the fingerprints. Their coordinates and direction are most distinctive features to represent the fingerprint. Most fingerprint matching systems store only the minutiae template in the database for further usage. The conventional methods to utilize minutiae information are treating it as a point set and finding the matched points from different minutiae sets. This kind of minutiae-based fingerprint recognition/matching systems consists of two steps: minutiae extraction and minutiae matching. Image enhancement, histogram equalization, thinning, binarization, smoothing, block direction estimation, image segmentation, ROI extraction etc. are discussed in the minutiae extraction step. After the extraction of minutiae the false minutiae are removed from the extraction to get the accurate result. In the minutiae matching process, the minutiae features of a given fingerprint are compared with the minutiae template and the matched minutiae will be found out. The final template used for fingerprint matching is further utilized in the matching stage to enhance the system's performance.

Fingerprint Recognition for Person Identification and Verification Based on Minutiae Matching

—There are various types of applications for fingerprint recognition which is used for different purposes .fingerprint is one of the challenging pattern Recognition problem. The Fingerprint Recognition system is divided into four stages. First is Acquisition stage to capture the fingerprint image ,The second is Pre-processing stage to enhancement , binarization ,thinning fingerprint image. The Third stage is Feature Extraction Stage to extract the feature from the thinning image by use minutiae extractor methods to extract ridge ending and ridge bifurcation from thinning.The fourth stage is matching(Identification, Verification) to match two minutiae points by using minutiae matcher method in which similarity and distance measure are used. The algorithm is tested accurately and reliably by using fingerprint images from different databases. In this paper the fingerprint databases used are FVC2000 and FVC2002 Databases, we see that ,the FVC2002 database perform better results compare with FVC2000 database. The recognition system evaluate with two factor FAR and FRR ,In this system the result of FAR is 0.0154 and FRR is 0.0137 with Accuracy equal to 98.55%.

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.