Review on Fingerprint Recognition (original) (raw)

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.

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.

Analysis of Fingerprint Minutiae Extraction and Matching Algorithm

2016

Biometrics is one of the most proficient authentication techniques and provides a method to validate a person to protect from any misleading actions. It can be used for personal authentication using physiological and behavioral features which are presumed to be characteristic for each individual. Due to its security-associated applications currently biometrics is the subject of intense research by academic institutions and private. However, each trait has its specific challenges and particular issues. Though various biometric techniques have certain concerns fingerprint is accepted by many researchers because fingerprint recognition systems has received a great deal of its easiness and believed to give effective solution to person authentication. It provides a powerful tool for access control, security and for real-world applications. Fingerprints are developing as the most common and trusted biometric for individual identification. The major objective of this study is to review the wide research that has been done on automatic fingerprint identification system based on minutiae extraction and matching algorithms. Minutiae features have most of a fingerprint's individuality, and furthermost important fingerprint feature for authentication systems. Minutiae extraction, matching algorithms, and identification/verification performance are discussed in detail with open problems and future directions acknowledged.

Fingerprint Authentication System Using Minutiae Matching and Application

2014

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 System (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.

AN EFFICIENT ALGORITHM FOR FINGERPRINT RECOGNITION USING MINUTIAE EXTRACTION

Fingerprints have always been considered as basic element for personal recognition. The performance of fingerprint recognition system depends on minutiae which are extracted from raw fingerprint images. In this study, an efficient scheme for fingerprint recognition was proposed. Initially, the input image was enhanced using pre-processing techniques. After image enhancement, image segmentation was performed and minutiae extraction was done using ridge thinning and minutiae marking. To this end, false minutiae removal was done prior to final match. In the proposed scheme, inter ridge distance was finely tuned to improve the overall sensitivity of fingerprint identification which also reduced FAR and FRR considerably. The proposed scheme was evaluated using a dataset of 500 images taken from FVC 2002, FVC 2004 and FVC 2006 and showed better performance as compared to the previous methods.

Designing of Fingerprint Recognition System Using Minutia Extraction and Matching

2015

A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. This work deals with the development of a highly robust and efficient biometric person identification system based on fingerprint features. Human fingerprints are rich in details called minutiae, which can be used as identification marks for fingerprint verification. The goal of this project is to develop a complete system for fingerprint verification through extracting and matching minutiae. To achieve good minutiae extraction in fingerprints with varying quality, pre-processing in form of image enhancement and binarization is first applied on fingerprints before they are evaluated. Many methods have been combined to build a minutia extractor and a minutia matcher. Minutia-marking with false minutiae removal methods are used in the work. An alignmentbased elastic matching algorithm has been developed for minutia matching. This algorithm is c...

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%.