scholarly journals A Review on Detection and Analysis of Fingerprint

2020 ◽  
pp. 51-56
Author(s):  
Bhavani Ranbida ◽  
Chandra Sekhar Panda
Keyword(s):  
Main Idea ◽  
Human Identification ◽  
Recognition System ◽  
Research Paper ◽  
Fingerprint Recognition ◽  
Fingerprint Analysis ◽  
Human Recognition ◽  
Finger Print ◽  
Image Format ◽  
Fingerprint Detection

Fingerprint analysis is the most essential part of human identification or human recognition. At present too many biometric techniques are presented for fingerprint identification and fingerprint recognition. We know that, a fingerprint contains a lot of key point like Y shape, delta, ridge ending, ridge staring, minutiae’s pattern and etc. All points are apodictic of unique for any human fingerprint. The aim of this paper is to review numerous recently work on fingerprint recognition system. Fingerprint detection is a very important topic to identify the correct person’s finger print and can make everything secure. The main idea of this research paper is to find out the 100% correct fingerprint details from any document (in image format). In this research paper we will get that how to identify the details of fingerprint from image.

scholarly journals Evaluation of Fingerprint Liveness Detection by Machine Learning Approach - A Systematic View

2021 ◽  
Vol 3 (1) ◽  
pp. 16-30
Author(s):  
Edriss Eisa Babikir Adam ◽  
Sathesh
Keyword(s):  
Machine Learning ◽  
Essential Element ◽  
Internet Banking ◽  
Ensemble Classifier ◽  
Machine Learning Algorithms ◽  
Fingerprint Recognition ◽  
Support Vector ◽  
Fingerprint Analysis ◽  
Developed Country ◽  
Fingerprint Detection

Recently, fake fingerprint detection is a challenging task in the cyber-crime sector in any developed country. Biometric authentication is growing in many sectors such as internet banking, secret file locker, etc. There spoof fingerprint detection is an essential element that is used to detect spot-on fingerprint analysis. This article focuses on the implementation and evaluation of suitable machine learning algorithms to detect fingerprint liveness. It also includes the comparative study between Ridge-let Transform (RT) and the Machine Learning (ML) approach. This article emphasis on research and analysis of the detection of the liveness spoof fingerprint and identifies the problems in different techniques and solutions. The support vector machine (SVM) classifiers work with indiscriminate loads and confined grayscale array values. This leads to a liveness report of fingerprints for detection purposes. The SVM methodology classifies the fingerprint images among more than 50K of real and spoof fingerprint image collections based on this logic. Our proposed method achieves an overall high accuracy of detection of liveness fingerprint analysis. The ensemble classifier approach model is proving an overall efficiency rate of 90.34 % accurately classifies samples than the image recognition method with RT. This recommended method demonstrates the decrement of 2.5% error rate when compared with existing methods. The augmentation of the dataset is used to improve the accuracy to detect. Besides, it gives fake fingerprint recognition and makes available future direction.

scholarly journals Design of Multi-Biometrics for Fake Fingerprint Detection through Body Odor

2019 ◽  
Vol 8 (4) ◽  
pp. 9646-9650
Keyword(s):  
Real Time ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Body Odor ◽  
Biometric System ◽  
Biometric Recognition ◽  
Odor Recognition ◽  
Biometric Systems ◽  
History Of ◽  
Fingerprint Detection

This work proposes the modernistic multibiometric recognition system for detecting artificial fingerprints and new biometric recognition system to use it in some real-time scenarios. In the recent studies of multi-biometrics, the usage of fingerprint and body odor recognition system stays untouched. This proposed design of a multi-biometric system includes a body odor recognition system along with a fingerprint recognition system that will improve the results in terms of accuracy. The reason behind proposing this model is to detect artificial fingerprints by differentiating the odor of human skin from other materials that are employed in the preparation of artificial fingerprints. This multi-biometric system can be used in forensic labs to identify criminals and to improve the standards of security in authentication of an individual. This multi-biometric system will completely eradicate the use of fake fingerprints and this proposed work will make a remarkable place in real-time applications and the history of multi-biometric systems.

Analysis of Fingerprint Recognition System Using Neural Network

2020 ◽  
Author(s):  
Ganesh Awasthi ◽  
Dr. Hanumant Fadewar ◽  
Almas Siddiqui ◽  
Bharatratna P. Gaikwad
Keyword(s):  
Neural Network ◽  
Recognition System ◽  
Fingerprint Recognition

scholarly journals Spectral Minutiae Fingerprint Recognition System

2014 ◽  
pp. 103-108
Author(s):  
S. Shanawaz Basha ◽  
N. Musrat Sultana
Keyword(s):  
Large Scale ◽  
Reduction Rate ◽  
Principal Component ◽  
Rejection Rate ◽  
Recognition System ◽  
Feature Reduction ◽  
Fingerprint Recognition ◽  
Identification System ◽  
Behavioral Characteristics ◽  
Template Size

Biometrics refers to the automatic recognition of individuals based on their physiological and/or behavioral characteristics, such as faces, finger prints, iris, and gait. In this paper, we focus on the application of finger print recognition system. The spectral minutiae fingerprint recognition is a method to represent a minutiae set as a fixedlength feature vector, which is invariant to translation, and in which rotation and scaling become translations, so that they can be easily compensated for. Based on the spectral minutiae features, this paper introduces two feature reduction algorithms: the Column Principal Component Analysis and the Line Discrete Fourier Transform feature reductions, which can efficiently compress the template size with a reduction rate of 94%.With reduced features, we can also achieve a fast minutiae-based matching algorithm. This paper presents the performance of the spectral minutiae fingerprint recognition system, this fast operation renders our system suitable for a large-scale fingerprint identification system, thus significantly reducing the time to perform matching, especially in systems like, police patrolling, airports etc,. The spectral minutiae representation system tends to significantly reduce the false acceptance rate with a marginal increase in the false rejection rate.

scholarly journals A multimodal biometric identification system based on cascade advanced of fingerprint, fingervein and face images

2020 ◽  
Vol 17 (3) ◽  
pp. 1562
Author(s):  
El mehdi Cherrat ◽  
Rachid Alaoui ◽  
Hassane Bouzahir
Keyword(s):  
Gabor Filter ◽  
Regression Line ◽  
Histogram Equalization ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Identification System ◽  
Fusion Algorithm ◽  
Decision Level ◽  
Face Images ◽  
Level Method

<p>In this paper, we present a multimodal biometric recognition system that combines fingerprint, fingervein and face images based on cascade advanced and decision level fusion. First, in fingerprint recognition system, the images are enhanced using gabor filter, binarized and passed to thinning method. Then, the minutiae points are extracted to identify that an individual is genuine or impostor. In fingervein recognition system, image processing is required using Linear Regression Line, Canny and local histogram equalization technique to improve better the quality of images. Next, the features are obtained using Histogram of Oriented Gradient (HOG). Moreover, the Convolutional Neural Networks (CNN) and the Local Binary Pattern (LBP) are applied to detect and extract the features of the face images, respectively. In addition, we proposed three different modes in our work. At the first, the person is identified when the recognition system of one single biometric modality is matched. At the second, the fusion is achieved at cascade decision level method based on AND rule when the recognition system of both biometric traits is validated. At the last mode, the fusion is accomplished at decision level method based on AND rule using three types of biometric. The simulation results have demonstrated that the proposed fusion algorithm increases the accuracy to 99,43% than the other system based on unimodal or bimodal characteristics.</p>

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