Finger-vein image recognition combining modified hausdorff distance with minutiae feature matching

In this paper, we propose a novel method for finger-vein recognition. We extract the features of the vein patterns for recognition. Then, the minutiae features included bifurcation points and ending points are extracted from these vein patterns. These feature points are used as a geometric representation of the vein patterns shape. Finally, the modified Hausdorff distance algorithm is provided to evaluate the identifica-tion ability among all possible relative positions of the vein patterns shape. This algorithm has been widely used for comparing point sets or edge maps since it does not require point cor-respondence. Experimental results show these minutiae feature points can be used to perform personal verification tasks as a geometric rep-resentation of the vein patterns shape. Fur-thermore, in this developed method. we can achieve robust image matching under different lighting conditions.

Biometric-PKI Authentication System Using Fingerprint Minutiae

A digital certificate under Public Key Infrastructure has a defect of Man-in-the-Middle Attack that performs hash collision attacks. In this paper, we propose a robust biometric-PKI authentication system against Man-in-the-Middle Attack. The biometric-PKI authentication system consists of current PKI authentication and biometric authentication, which employs biometric data and a public key from a digital certificate. In the proposed biometric-PKI authentication system, an au- thentication process performs that it extracts consistent features of fingerprint images, encrypts consistent features, and matches features with prepared templates. The simulation results of the proposed authentication system prove that our system achieves low false acceptance rate and high accuracy rate.

A Fingerprint Minutiae Matching Method Based on Line Segment Vector

Minutiae-based fingerprint matching is the most commonly used in an automatic fingerprint identification system. In this paper, we propose a minutia matching method based on line segment vector. This method uses all the detected minutiae （the ridge ending and the ridge bifurcation） in a fingerprint image to create a set of new vectors （line segment vector）. Using these vectors, we can determine a truer reference point more efficiently. In addition, this new minutiae vector can also increase the accuracy of the minutiae matching. By experiment on the public domain collections of fingerprint images fvc2004 DID set A and DB4 set A, the result shows that our algorithm can obtain an improved verification performance.

Fingerprint Enhancement, Minutiae Extraction and Matching Techniques

Nowadays, it is a new technology era. Fingerprint is necessary identification recognition of citizens. Fingerprint technology has become more popular and connected to human being life and come to replace traditional identification and verifying recognition today. The fingerprint will continue to substitute the ID of citizens as soon as possible in the future. Fingerprint refers to a complex of combination between gap of ridges and valleys on all of the fingertips. Clearer ridges quality is more convenient to analyze who you are and system can recognize your unique identity. Poorer ridges quality image is a significant problem that system has to improve and enhance the images quality before analyzing the results. Dry and wet ridges are the main issues that developers and researchers need to work on as it provides poor quality image. Medium ridge image is a good condition for analysis, but it also needs to be improved. Therefore, fingerprint images have to control the clearer quality and computing minutiae result and then comparing to templates, which stored in the database. The result will display if it is matched but it will not appear when that person has not yet registered. The paper proposed three algorithms to enhance image, extract minutiae and match with fingerprint templates. The first step, is used to enhance the image quality using brightness and Gabor filters on the fingerprint surface to make ridgelines darker. The second step is to extract minutia. It used to convert the images to binary (0 and 1) and process thinning image with Zhang Suen algorithms. Then, the pictures go through the fixing procedure to correct any missed links, error ridges or spurious minutiae that generated by fingerprint algorithms before they undergo final analysis, calculate location of minutiae and the total of the minutiae on the fingerprint surface. The last step is matching algorithms that can be proof of a person identity by comparing minutiae result with those in the database. If a person has already enrolled, the result will confirm.

Accurate Fingerprint Enhancement and Identification Using Minutiae Extraction

Fingerprints are an extraordinary source for recognizable proof of people. Unique finger impression acknowledgment is one of the most seasoned types of biometric identification. However, getting a decent unique finger impression picture isn’t that simple. So we must process unique finger impression picture before coordinating. A crucial advance in measurements of fingerprint minutiae is to obtain minutiae from the finger impression pictures dependably. However, fingerprint images are occasionally of perfect quality. They might be debased and defiled because of varieties in skin and impression conditions. Along these lines, image enhancement strategies utilize other details extraction to acquire a more reliable estimation of minutiae areas. The primary objective of this research work is to introduce a superior and improved unique fingerprint image. We studied the elements identifying with getting elite component focuses detection algorithm, for example, picture quality, segmentation, picture upgrade and highlight recognition. Usually utilized features for enhancing unique finger impression picture quality are Fourier spectrum energy, Sobel filter energy, and local orientation. Precise segmentation of unique finger impression edges from a broad foundation is vital. For productive improvement and feature extraction algorithms, we zero the commotion in segmented features. As a pre-processing method, we need to perform comprising of field introduction, ridge frequency estimation, Sobel filtering, division. Then connect the resulting picture to a thinning algorithm and consequent minutiae extraction. After resultant extraction of these minutiae focuses, we will utilize the picture with focuses for coordinating or finding the offenders and also for other security issues. The procedure of image pre-processing and minutiae extraction is explored. The simulations are performed in the MATLAB environment to assess the execution of the implemented algorithm.

Fingerprint Matching Based on Local Relative Orientation Field

A fingerprint matching method based on local relative orientation field is proposed. It extracts local relative orientation field around each minutia for minutiae matching. Local orientation features are also used to sorting minutiae in order to speed up searching a minutia when pairing minutiae. The experimental result reveals that this method achieves improved recognition accuracy. Key words fingerprint matching - ridge-based minutiae matching - local relative orientation field - sorting minutiae CLC number TP 391 Foundation item: Supported by the National Natural Science Foundation of China (60373023)Biography: ZHU En (1976-), male, Ph. D candidate, research direction: pattern recognition, image processing and information security.

FINGERPRINT-BASED KEY BINDING/RECOVERING SCHEME BASED ON FUZZY VAULT

This letter proposes fingerprint-based key binding/recovering with fuzzy vault. Fingerprint minutiae data and the cryptographic key are merged together by a multivariable linear function. First, the minutiae data are bound by a set of random data through the linear function. The number of the function’s variables is determined by the required number of matched minutiae. Then, a new key de- rived from the random data is used to encrypt the cryptographic key. Lastly, the binding data are protected using fuzzy vault scheme. The proposed scheme provides the system with the flexibility to use changeable number of minutiae to bind/recover the protected key and a unified method regardless of the length of the key.

Fingerprint Feature Extraction Based on Macroscopic Curvature

In the Automatic Fingerprint Identification Syst e m (AFIS), extracting the feature of fingerprint is very important. The local cur vature of ridges of fingerprint is irregular, so people have the barrier to effe ctively extract the fingerprint curve features to describe fingerprint. This art icle proposes a novel algorithm; it embraces information of few nearby fingerpri nt ridges to extract a new characteristic which can describe the curvature featu re of fingerprint. Experimental results show the algorithm is feasible, and the characteristics extracted by it can clearly show the inner macroscopic curve pro perties of fingerprint. The result also shows that this kind of characteristic i s robust to noise and pollution.

Fingerprint Identification by Artificial Neural Network

Biometric techniques require critical operations of digital processing for identification of individuals. In this context, this paper aims to develop a system for automatic processing of fingerprint identification by their minutiae using Artificial Neural Networks （ANN）, which reveals to be highly effective. The ANN method implemented is a based on Multi-Layer Perceptron （MLP） model, which utilizes the algorithm of retro-propagation of gradient during the learning process. In such a process, the mean square error generated represents the specific parameter for the identification phase by comparing a fingerprint taken from a crime scene with those of a reference database.

Innovation Research on Fingerprint and DNA Identifications

In this study,two models for fingerprint and DNA identifications are constructed based on modern technologies,while offering significant advances over prior models.Our models have high credibility,obtaining relatively accurate results under different circumstances.Under different assumptions,this model tests the probability of the validity in the statement that human fingerprints are unique to be 93.94%.In other words,the percentage of misidentification is 6.06%.This model is a robust fingerprint identification method that can tolerate highly nonlinear deformations.The model is tested on the basis of a self-built database,proving that the model has high credibility,and convincing results are obtained from sensitivity analysis.In order to estimate the odds of misidentification by DNA evidence,we emphasized on two factors that might contribute to misidentification:random match probability and the probability arising from laboratory errors.Then,a model is developed using Bayes’theorem to reveal the inherent relationship between them,which carries some reference value.The probability of matching by DNA evidence is estimated based on the changes in the significant level.Finally,the probabilities of misidentification by both fingerprint evidence and DNA evidence are compared using numerous data.We found that the probability of the former is 6.06%and that of the latter is smaller than 4.0 x 10^(−10).Therefore,it can be concluded that DNA identification is far better than that of fingerprint identification.

Fingerprints Matching Based on the Integrated Template

Traditional fingerprints matching approaches are based on the minutiae and the texture characteristics. It is difficult for today's techniques to deal with the images captured by the solid-state fingerprint sensors because the sampling area of these sensors is very small and only portion of the fingerprint can be obtained. In this paper, a novel integrated template has been built using several images and combining the minutiae with the texture characteristics at the same time. By using a kind of Gabor filters (eight directions) on the region around the reliable minutiae, local texture of each minutia termed as minutiacode is extracted. Further, a real-time matching algorithm using the integrated template is presented. The output is determined by the weight factor of the compound matching. Finally, experimental results show that this system performs well in reducing false reject rate (FRR).

Accuracy and reliability of feature selection by Chinese fingerprint examiners

The selection of minutiae is a critical part of the analysis phase within the fingerprint identification methodology,known as analysis-comparison-evaluation-verification.This study assessed the accuracy and reliability of the minutiae selections of 92 Chinese fingerprint examiners during the analysis phase,absent an exemplar print,of the fingerprint identification process.Specifically,we measured the accuracy(trueness)of their annotation of minutiae,and we measured their reliability which is the reproducibility and repeatability in their annotations in one complex mark by using R software.We observed significant variation within inter-and intra-examiner annotations of the minutiae.We saw no statistically significant differences for the variability of minutiae annotations based on the participant’s sex or years of experience.

Deep learning compact binary codes for fingerprint indexing

With the rapid growth in fingerprint databases, it has become necessary to develop excellent fingerprint indexing to achieve efficiency and accuracy. Fingerprint indexing has been widely studied with real-valued features,but few studies focus on binary feature representation, which is more suitable to identify fingerprints efficiently in large-scale fingerprint databases. In this study, we propose a deep compact binary minutia cylinder code（DCBMCC）as an effective and discriminative feature representation for fingerprint indexing. Specifically, the minutia cylinder code（MCC）, as the state-of-the-art fingerprint representation, is analyzed and its shortcomings are revealed.Accordingly, we propose a novel fingerprint indexing method based on deep neural networks to learn DCBMCC.Our novel network restricts the penultimate layer to directly output binary codes. Moreover, we incorporate independence, balance, quantization-loss-minimum, and similarity-preservation properties in this learning process.Eventually, a multi-index hashing（MIH） based fingerprint indexing scheme further speeds up the exact search in the Hamming space by building multiple hash tables on binary code substrings. Furthermore, numerous experiments on public databases show that the proposed approach is an outstanding fingerprint indexing method since it has an extremely small error rate with a very low penetration rate.