An algorithm for fingerprint image postprocessing (original) (raw)
Related papers
Effective and efficient fingerprint image postprocessing
7th International Conference on Control, Automation, Robotics and Vision, 2002. ICARCV 2002., 2002
Minutiae extraction is a crucial step in an automatic fingerprint identification system. However, the presence of noise in poor-quality images causes a large number of extraction errors, including the dropping of true minutiae and production of false minutiae. A study on these errors reveals that postprocessing is effective in removing false minutiae while keeping true ones. Furthermore, the overall processing efficiency could be improved because of the reduction in total minutia number. In this paper, we present a novel fingerprint image postprocessing algorithm. It is developed based on several rules, which are generalized through a study on the errors that commonly occur in minutiae extraction and their effects on the overall verification performance. Thorough experimental tests demonstrate the proposed postprocessing algorithm to be both effective and efficient.
Removal of False Minutiae with Fuzzy Rules from the Extracted Minutiae of Fingerprint Image
Advances in Intelligent and Soft Computing, 2012
Human fingerprints are rich in details called minutiae, which can be used as identification marks for fingerprint verification. Minutiae are the two most prominent and well-accepted classes of fingerprint features arising from local ridge discontinuities: ridge endings and ridge bifurcations. In today's world minutia matching is most popular and modern technology for fingerprint matching. .If there is enough minutia point in one fingerprint image that are corresponding to other fingerprint image, then it is most likely that both images are from the same finger print. In this paper, we proposed a complete system for minutiae extraction and removing the false minutiae from the extracted ones. The main objective of this paper is developing a new idea for extracting minutiae points and removing the false minutiae by implementing some fuzzy rules. It comprises of various steps. It begins with the acquisition of the fingerprint image. This is followed by binarization ie, converting the gray image to binary image and then thinning ie, making the ridges just one pixel wide. Finally the minutiae points are extracted based on Tico and Kuosmanen[1] and the Crossing Number(CN) method. Then, among the extracted minutiae, false minutiae are removed with fuzzy rules. Thus our system could be a better pre-processing technique for authentication.
NOVEL APPROACHES OF BIOMETRIC FINGER PRINT MINUTIAE DETECTION AND EXTRACTION PROCESS
The most common use of biometric identification method is fingerprint recognition. Fingerprints are unique for every person. Biometric Fingerprint identification has immense in forensic science & criminal investigations. The automatic fingerprint recognition systems are based on local ridge features called as minutiae. Minutiae are automatic identification systems based on ridge bifurcations and terminations. Hence it is extremely important to mark these minutiae accurately and reject the false ones. However, prone to degradation and corruption of fingerprint images due to certain factors such that skin variations and impression such as dirt, humidity, scars and non-uniform. We should apply some image enhancement techniques before minutiae extraction.
Minutiae Extraction from Fingerprint Images a Review
Fingerprints are the oldest and most widely used form of biometric identification. Everyone is known to have unique, immutable fingerprints. As most Automatic Fingerprint Recognition Systems are based on local ridge features known as minutiae, marking minutiae accurately and rejecting false ones is very important. However, fingerprint images get degraded and corrupted due to variations in skin and impression conditions. Thus, image enhancement techniques are employed prior to minutiae extraction. A critical step in automatic fingerprint matching is to reliably extract minutiae from the input fingerprint images. This paper presents a review of a large number of techniques present in the literature for extracting fingerprint minutiae. The techniques are broadly classified as those working on binarized images and those that work on gray scale images directly.
Fingerprint identification and verification system using minutiae matching
2004
Fingerprints are the most widely used biometric feature for person identification and verification in the field of biometric identification. Fingerprints possess two main types of features that are used for automatic fingerprint identification and verification: (i) global ridge and furrow structure that forms a special pattern in the central region of the fingerprint and (ii) Minutiae details associated with the local ridge and furrow structure. This paper presents the implementation of a minutiae based approach to fingerprint identification and verification and serves as a review of the different techniques used in various steps in the development of minutiae based Automatic Fingerprint Identification Systems (AFIS). The technique conferred in this paper is based on the extraction of minutiae from the thinned, binarized and segmented version of a fingerprint image. The system uses fingerprint classification for indexing during fingerprint matching which greatly enhances the performance of the matching algorithm. Good results (~92% accuracy) were obtained using the FVC2000 fingerprint databases.
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.
Determination of Minutiae Scores for Fingerprint Image Applications
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 fingerprint image that has undergone binarization, followed by thinning, in order to extract the minutiae, contains 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 have given higher relevance and importance in the process of minutiae matching. In this work, a method to assign a score value to each of the extracted minutiae is proposed, based on some topographical properties of a minutia. The score associated to a minutia signifies its genuineness and prominence. A minutia with a higher score value should be given higher priority in the matching scheme to yield better results.
In Fingerprint recognition, after the initial preprocessing, the feature is extracted from the Fingerprint thinned image. Extraction of crucial and beneficial capabilities or features of interest from a fingerprint image is an essential venture during recognition. Feature extraction algorithms pick handiest or only applicable features important for enhancing the performance of matching and recognition rate and outcomes with the feature vector. The feature extraction algorithms or techniques require only relevant features like minutiae details and do not require any background details or domain-specific details. They need to be smooth or easy to compute with a purpose to gain a viable or practicable technique for a huge image series. Minutiae details or fingerprint ridge ending or bifurcation details using skeletonized or thinning approach is a very popular method for feature extraction. The preprocessed thinned image is further post-processed to remove some false minutiae from minutiae table and which is generated through crossing number theory. One more purpose of post-processing is to reduce the number of minutiae points by removing false minutiae structures like spurs, ride breaks, short ridge, holes or islands, bridges, and ladders. In this paper w × w window neighborhood is considered for each minutia in Minutiae Table. Minutiae Table contains Ridge ending or bifurcation code as 1 or 3 with its location details means x and y position in two columns and the sum of these details as its fourth column. These Minutiae tables are used for generating Fingerprint Hash code, which can be used as index-or identity key in order to uniquely identify an individual person or as one factor in Multifactor Authentication Model.
Fingerprint Reorganization Using Minutiae Based Matching for Identification and Verification
International Journal of Science and Research (IJSR), 2016
Fingerprints play distinguishing role in biometrics. Fingerprints are the most widely used parameter for personal identification amongst all biometric based personal authentication systems. They give unique identification to the individual. They are permanent and non changing character pattern. As most automatic fingerprint recognition systems are based on local features of ridge known as minutiae, marking minutiae accurately and rejecting false ones is critically important. This paper is a study and implementation of a fingerprint recognition system based on Minutiae based matching which is quite frequently used in various fingerprint algorithms and techniques. This approach mainly involves extraction of minutiae points from the sample fingerprint images and then performing fingerprint matching based on the score of minutiae pairings among two fingerprints. Our implementation mainly assimilates image enhancement, image segmentation, feature extraction and minutiae matching. It finally generates a result which tells whether two fingerprints match or not.
Fingerprint verification system using minutiae extraction technique
World Academy of Science, …, 2008
Most fingerprint recognition techniques are based on minutiae matching and have been well studied. However, this technology still suffers from problems associated with the handling of poor quality impressions. One problem besetting fingerprint matching is distortion. Distortion changes both geometric position and orientation, and leads to difficulties in establishing a match among multiple impressions acquired from the same finger tip. Marking all the minutiae accurately as well as rejecting false minutiae is another issue still under research. Our work has combined many methods to build a minutia extractor and a minutia matcher. The combination of multiple methods comes from a wide investigation into research papers. Also some novel changes like segmentation using Morphological operations, improved thinning, false minutiae removal methods, minutia marking with special considering the triple branch counting, minutia unification by decomposing a branch into three terminations, and matching in the unified x-y coordinate system after a two-step transformation are used in the work.