Fingerprint Recognition for Person Identification and Verification Based on Minutiae Matching

2016 ◽  
Author(s):  
Mouad. M.H. Ali ◽  
Vivek H. Mahale ◽  
Pravin Yannawar ◽  
A.T. Gaikwad
Keyword(s):  
Fingerprint Recognition ◽  
Person Identification ◽  
Minutiae Matching

scholarly journals The Study of Three-Dimensional Fingerprint Recognition in Cultural Heritage

2021 ◽  
Vol 14 (4) ◽  
pp. 1-20
Author(s):  
Dzemila Sero ◽  
Isabelle Garachon ◽  
Erma Hermens ◽  
Robert Van Liere ◽  
Kees Joost Batenburg
Keyword(s):  
Image Processing ◽  
Cultural Heritage ◽  
Three Dimensional ◽  
Fingerprint Recognition ◽  
Person Identification ◽  
Imaging Technologies ◽  
The Past ◽  
Reconstruction Methods ◽  
Cultural Systems ◽  
Digital Fingerprint

Fingerprints play a central role in any field where person identification is required. In forensics and biometrics, three-dimensional fingerprint-based imaging technologies, and corresponding recognition methods, have been vastly investigated. In cultural heritage, preliminary studies provide evidence that the three-dimensional impressions left on objects from the past (ancient fingerprints) are of paramount relevance to understand the socio-cultural systems of former societies, to possibly identify a single producer of multiple potteries, and to authenticate the artist of a sculpture. These findings suggest that the study of ancient fingerprints can be further investigated and open new avenues of research. However, the potential for capturing and analyzing ancient fingerprints is still largely unexplored in the context of cultural heritage research. In fact, most of the existing studies have focused on plane fingerprint representations and commercial software for image processing. Our aim is to outline the opportunities and challenges of digital fingerprint recognition in answering a range of questions in cultural heritage research. Therefore, we summarize the fingerprint-based imaging technologies, reconstruction methods, and analyses used in biometrics that could be beneficial to the study of ancient fingerprints in cultural heritage. In addition, we analyze the works conducted on ancient fingerprints from potteries and ceramic/fired clay sculptures. We conclude with a discussion on the open challenges and future works that could initiate novel strategies for ancient fingerprint acquisition, digitization, and processing within the cultural heritage community.

scholarly journals School Debit Transaction Using Fingerprint Recognition System

2019 ◽  
Vol 1 (2) ◽  
Author(s):  
Wai Kit Wong ◽  
Thu Soe Min ◽  
Shi Enn Chong
Keyword(s):  
Labour Force ◽  
Recognition System ◽  
Payment System ◽  
Fingerprint Recognition ◽  
Biometric Technology ◽  
Cash Payment ◽  
User Need ◽  
Minutiae Matching

This paper proposed a fingerprint based school debit transaction system using minutiae matching biometric technology. This biometric cashless transaction system intensely shortens the luncheon line traffic and labour force compared to conventional cash payment system. Furthermore, contrast with card cashless transaction system, fingerprint cashless transaction system with benefit that user need not carry additional identification object and remember lengthy password. The implementation of this cashless transaction system provides a more organize, reliable and efficient way to operate the school debit transaction system. 

scholarly journals Implementation of a Transform-Minutiae Fusion-Based Model for Fingerprint Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Justice Kwame Appati ◽  
Prince Kofi Nartey ◽  
Ebenezer Owusu ◽  
Ismail Wafaa Denwar
Keyword(s):  
Fingerprint Recognition ◽  
Person Identification ◽  
Data Smoothing ◽  
Scientific Methods ◽  
Behavioral Traits ◽  
Person Recognition ◽  
Accuracy Measure ◽  
Palm Print ◽  
The Many ◽  
Wave Atom Transform

Biometrics consists of scientific methods of using a person’s unique physiological or behavioral traits for electronic identification and verification. The traits for biometric identification are fingerprint, voice, face, and palm print recognition. However, this study considers fingerprint recognition for in-person identification since they are distinctive, reliable, and relatively easy to acquire. Despite the many works done, the problem of accuracy still persists which perhaps can be attributed to the varying characteristic of the acquisition devices. This study seeks to improve the issue recognition accuracy with the proposal of the fusion of a two transform and minutiae models. In this study, a transform-minutiae fusion-based model for fingerprint recognition is proposed. The first transform technique, thus wave atom transform, was used for data smoothing while the second transform, thus wavelet, was used for feature extraction. These features were added to the minutiae features for person recognition. Evaluating the proposed design on the FVC 2002 dataset showed a relatively better performance compared to existing methods with an accuracy measure of 100% as to 96.67% and 98.55% of the existing methods.

The Research of Algorithms for Fingerprint Characteristic Extraction and Matching

2012 ◽  
Vol 433-440 ◽  
pp. 3479-3482
Author(s):  
Zhen Zhang ◽  
Li Liu
Keyword(s):  
High Efficiency ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Identification System ◽  
Fingerprint Image ◽  
Minutiae Extraction ◽  
Characteristic Extraction ◽  
System A ◽  
Fingerprint Minutiae ◽  
Minutiae Matching

Fingerprint recognition plays an important role in identification of organism characters. Automatic fingerprint identification system(AFIS)is a technology based on computer or microprocessor with advantages of convenience and high efficiency. The extraction and matching of fingerprint minutiae is a necessary step in automatic fingerprint recognition system. A set of algorithms for minutiae extraction and minutiae matching of fingerprint image are proposed in this paper based on the analysis of the inherent minutiae of fingerprint.

scholarly journals Fingerprint Recognition Using Local Features and Hu Moments

2012 ◽  
Vol 10 (5) ◽  
Author(s):  
G. Aguilar-Torres ◽  
G. Sánchez-Pérez ◽  
K. Toscano-Medina ◽  
H. Pérez-Meana
Keyword(s):  
Fourier Transform ◽  
False Recognition ◽  
Local Features ◽  
Fingerprint Recognition ◽  
Recognition Method ◽  
Person Identification ◽  
Invariant Moments ◽  
Hu Moments ◽  
Curve Track ◽  
High Degree

Person identification systems based on fingerprint patterns called Automatic Fingerprint Identification Systems, AFIS,are some of the most widely used biometric methods since they provide a high degree of success. The accuracy ofAFIS is mainly due to some unique characteristics called minutiae, which are points where a curve track finishes,intersects with another curve track, or branches off. During past decades several efficient minutia-based fingerprintrecognition algorithms have been proposed which achieve false recognition rates close to 1%, however, theirrecognition rate may be still improved. To this end, this paper presents a fingerprint recognition method using acombination of the Fast Fourier Transform (FFT) with Gabor filters for image enhancement. Next, fingerprintrecognition is carried out using a novel recognition stage based on Local Features and Hu invariant moments forverification.

Human Ear Pattern Recognition System

2017 ◽  
Vol 7 (8) ◽  
pp. 117 ◽  
Author(s):  
V. Jagan Naveen ◽  
K. Krishna Kishore ◽  
P. Rajesh Kumar
Keyword(s):  
Pattern Recognition ◽  
Expectation Maximization Algorithm ◽  
Recognition System ◽  
Modern World ◽  
Person Identification ◽  
Biometric Systems ◽  
Different Types ◽  
Recognition Systems ◽  
Human Ear ◽  
Pca Algorithm

In the modern world, human recognition systems play an important role to   improve security by reducing chances of evasion. Human ear is used for person identification .In the Empirical study on research on human ear, 10000 images are taken to find the uniqueness of the ear. Ear based system is one of the few biometric systems which can provides stable characteristics over the age. In this paper, ear images are taken from mathematical analysis of images (AMI) ear data base and the analysis is done on ear pattern recognition based on the Expectation maximization algorithm and k means algorithm.  Pattern of ears affected with different types of noises are recognized based on Principle component analysis (PCA) algorithm.

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