scholarly journals A Study on Machine Learning Approach for Fingerprint Recognition System

2019 ◽  
Vol 5 (11) ◽  
Author(s):  
Aayushi Tamrakar ◽  
Neetesh Gupta
Keyword(s):  
Rejection Rate ◽  
Recognition System ◽  
Extraction Methods ◽  
Acceptance Rate ◽  
Fingerprint Recognition ◽  
Fingerprint Classification ◽  
Machine Learning Approach ◽  
Optimum Approach ◽  
Authentication System ◽  
Recognition Technique

A biometric system is an evolving technology that is used in various fields like forensics, secured area and security system. Authentication system like fingerprint recognition is most commonly used biometric authentication system. Fingerprint method of identification is the oldest and widely used method of authentication used in biometrics. There are several reasons like displacement of finger during scanning, environmental conditions, behavior of user, etc., which causes the reduction in acceptance rate during fingerprint recognition. The result and accuracy of fingerprint recognition depends on the presence of valid minutiae. Fingerprint Recognition system designed uses various techniques in order to reduce the False Acceptance Rate (FAR) and False Rejection Rate (FRR) and to enhance the performance of the system. This paper reviews the fingerprint classification including feature extraction methods and learning models for proper classification to label different fingerprints. A comparative study of different recognition technique along with their limitations is also summarized and optimum approach is proposed which may enhance the performance of the system.

scholarly journals A Dual Security Scheme Based on DWT for Personnel Authentication

2019 ◽  
Vol 9 (1) ◽  
pp. 598-602
Keyword(s):  
Rejection Rate ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Identification System ◽  
Discrete Wavelet ◽  
Number Systems ◽  
Biometric Systems ◽  
The Face ◽  
Authentication System ◽  
Facial Recognition System

A biometric identification system that audits the presence of a person using real or behavioral features is safer than passwords and number systems. Present applications are mostly recognize an individual using the single modal biometric system. However, a single characteristic sometimes fails to authenticate accurately. Multimodal biometric technologies solve the problems that exist in the single biometric systems. It is very hard to identify images with low lighting environments using facial recognition system. By utilizing fingerprint recognition, this issue can be better addressed. This paper presents a dual personnel authentication system that incorporates face and fingerprint to improve security. For face identification, the Discrete Wavelet Transform (DWT) algorithm is used to acquire features from the face and fingerprint pictures. The technique used to integrate fingerprint and face is decision level fusion. By adding fingerprint recognition to the scheme, the proposed algorithm decreases the false rejection rate (FRR) in the face and fingerprint recognition and hence increases the accuracy of the authentication.

scholarly journals Low Resolution Fingerprint Image Verification using CNN Filter and LSTM Classifier

2020 ◽  
Vol 8 (5) ◽  
pp. 3546-3549
Keyword(s):  
Short Term Memory ◽  
Research Work ◽  
Recognition System ◽  
Extraction Methods ◽  
Fingerprint Recognition ◽  
Biometric System ◽  
Fingerprint Verification ◽  
Optimum Approach ◽  
Reduced Rate ◽  
Result Analysis

A biometric system is an evolving technology that is used in various fields like forensics, secured area and security system. One of the main biometric system is fingerprint recognition system. The reduced rate of performance of fingerprint verification system is due to many reasons such as displacement of finger during scanning, moisture on scanner, etc. The result and accuracy of fingerprint recognition depends on the presence of valid minutiae. According to literature several Fingerprint Recognition System are designed that uses various techniques in order to reduce false detection rate and to enhance the performance of the system. A comparative study of different recognition technique along with their limitations is also summarized and optimum approach is proposed which may enhance the performance of the system. This research work is focused on designing of fingerprint verification/classification including feature extraction methods and learning models for proper classification to label different fingerprints. In order to gain above mentioned objectives, FVC2002 dataset is taken for training and testing. In this dataset there are approx. 72 images which are used for testing purpose. In this dataset there are some blur, distorted as well as partial images also which are considered for recognition. Convolution Neural Network (CNN) and Long Short Term Memory (LSTM) is used for recognition of fingerprint. The result analysis shows approx. 3% enhancement over existing work.

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 Adaptive Threshold for Fingerprint Recognition System Based on Threat Level and System Load

2019 ◽  
Author(s):  
Mehul Raval ◽  
Vaibhav B Joshi
Keyword(s):  
Fuzzy Inference ◽  
Rejection Rate ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Inference System ◽  
Acceptance Threshold ◽  
System Load ◽  
Threat Level ◽  
Artificial Neural Network Ann ◽  
Matching Score

Fingerprint is widely used trait for person recognition in civilian applications. A user is authenticated when matching score is greater than acceptance threshold. The performance of fingerprint system (FS) is evaluated based on false acceptance rate (FAR) and false rejection rate (FRR). Usually the FS is set to work at a rate where FAR and FRR are equal (EER). However, operating at EER allows finite FAR which is risky during critical threat. In response acceptance threshold must shifts towards zero FAR to mitigate threat. This increases FRR, system load and user inconvenience. In civilian application acceptance threshold is set by vendor and currently there is no research attempt to change it dynamically. This is necessary as; 1) system must respond to external parameters like load and threat level; 2) system must balance security and user convenience due to high traffic?c. This paper describes a method to change acceptance threshold over the interval EER to zero FAR based on system load and threat level. The proposed method is based on fuzzy inference system (FIS) and artificial neural network (ANN).

scholarly journals Research and implementation of integrated fingerprint classification system based on backtracking feedback

2018 ◽  
Vol 246 ◽  
pp. 03030
Author(s):  
Han Jian Ning
Keyword(s):  
Classification System ◽  
Recognition Rate ◽  
Research Direction ◽  
Recognition System ◽  
Feedback Mechanism ◽  
Fingerprint Recognition ◽  
Fingerprint Classification ◽  
Intelligent Recognition ◽  
Important Research Direction ◽  
Experimental Comparisons

Fingerprint classification has always been an important research direction in the field of intelligent recognition. Based on the method of fingerprint classifier integration, the backtracking feedback mechanism is introduced, and a fingerprint classification system with high recognition rate is designed. Through the use of 1000 fingerprint images in the fingerprint library to test, The system show the recognition results due to the current Kalle Karu, anli K.jain design of a variety of fingerprint recognition system. Through a series of experimental comparisons, it is proved that the fingerprint classification recognition system with the feedback mechanism has better ability of fingerprint recognition, and greatly reduces the error rate of system recognition.

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

Multimodal score level fusion for recognition using face and palmprint

2020 ◽  
pp. 002072092092966
Author(s):  
Milind E Rane ◽  
Umesh S Bhadade
Keyword(s):  
Recognition System ◽  
Acceptance Rate ◽  
Face Database ◽  
Biometric Recognition ◽  
False Acceptance Rate ◽  
Score Level Fusion ◽  
Score Fusion ◽  
Matching Score ◽  
Level Fusion ◽  
Fusion Approach

The paper proposes a t-norm-based matching score fusion approach for a multimodal heterogenous biometric recognition system. Two trait-based multimodal recognition system is developed by using biometrics traits like palmprint and face. First, palmprint and face are pre-processed, extracted features and calculated matching score of each trait using correlation coefficient and combine matching scores using t-norm based score level fusion. Face database like Face 94, Face 95, Face 96, FERET, FRGC and palmprint database like IITD are operated for training and testing of algorithm. The results of experimentation show that the proposed algorithm provides the Genuine Acceptance Rate (GAR) of 99.7% at False Acceptance Rate (FAR) of 0.1% and GAR of 99.2% at FAR of 0.01% significantly improves the accuracy of a biometric recognition system. The proposed algorithm provides the 0.53% more accuracy at FAR of 0.1% and 2.77% more accuracy at FAR of 0.01%, when compared to existing works.

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