Morphological enhancement and triangular matching for fingerprint recognition (original) (raw)
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Fingerprint Image Enhancement Using Directional Morphological Filter
EUROCON 2005 - The International Conference on "Computer as a Tool", 2005
Fingerprint images quality enhancement is a topic phase to ensure good performance in an Automatic Fingerprint Identification System (AFIS) based on minutiae matching. In this paper a new fingerprint enhancement algorithm based on morphological filter is introduced. The algorithm is based on tree steps: directional decomposition, morphological filter and composition. 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.
Fingerprint Identification System using Tree Based Matching
International Journal of Computer Applications, 2012
With the increasing focus on the automatic personal identification applications, biometrics specifically fingerprint identification is the most reliable, secure and widely accepted technique. The automatic fingerprint identification systems have two important steps, such as fingerprint (a) image enhancement and (b) minutiae matching. In this paper, we develop a fingerprint image enhancement as well as matching algorithm based on directional curvature technique (DCT) of local ridges and a modified Tree based matching approach. In the preprocessing stage, the Fingerprint is De-noised, Binarised, Thinned and the approximate core points are calculated by DCT algorithm. The Minutiae points are extracted by template filtering over the image. Identifying all the minutiae accurately as well as rejecting false minutiae is another issue, addressed in this paper. The Minutiae Matching Score is determined using a modified Tree Matching algorithm with assigned probability value with its level priority. The study reveals that the proposed modified Tree Matching algorithm has better matching percentage for different fingerprints as well as low quality fingerprint image compared to the existing algorithms.
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.
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.Γs 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 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
Partial Fingerprint Image Enhancement using Region Division Technique and Morphological Transform
Nucleus, 2015
Fingerprints are the most renowned biometric trait for identification and verification. The quality of fingerprint image plays a vital role in feature extraction and matching. Existing algorithms work well for good quality fingerprint images and fail for partial fingerprint images as they are obtained from excessively dry fingers or affected by disease resulting in broken ridges. We propose an algorithm to enhance partial fingerprint images using morphological operations with region division technique. The proposed method divides low quality image into six regions from top to bottom. Morphological operations choose an appropriate Structuring Element (SE) that joins broken ridges and thus enhance the image for further processing. The proposed method uses SE βlineβ with suitable angle π and radius π in each region based on the orientation of the ridges. The algorithm is applied to 14 low quality fingerprint images from FVC-2002 database. Experimental results show that percentage accur...
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 triangular feature to improve the matching process. The result shows that it has improvement in recognition process.
Fingerprint Image Enhancement and Extraction of Minutiae and Orientation
Fingerprints are popular among the biometric β 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.
PROPOSAL TO ENHANCE FINGERPRINT RECOGNITION SYSTEM
This research concentrates on particular aspect, that is: Build Fingerprint Recognition System as in traditional but with modest suggested strategy aim to introduce an optimal fingerprint image feature's vector to the person and then considers it to be stored in database for future matching.
Communications on Applied Electronics, 2015
Extracting features from a fingerprint image relies mainly on the pre-processing stages the fingerprint has gone through. When the fingerprint image that has been captured is good enough then the final matching stage will produce a satisfying output. But many a times the image which is captured suffers from contact problems such as non-uniform contact, inconsistent contact and irreproducible contact.Because of such adverse and unpredictable image acquisition situations, a biometric system's (Fingerprint Recognition System) performance suffers from random false rejects/accepts. Hence the need for the pre-processing of an image becomes necessary. In this paper, pre-processing steps of spatial filtering and morphological operation in addition to Gabor filtering are introduced and comparative analyses of the three are done in MATLAB. It has been found that there is a significant removal of false minutiae in the step of minutiae extraction, if spatial or morphological filtering methods are introduced prior to Gabor filtering.