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 Hybrid Biometric Recognition using Stacked Auto Encoder with Random Forest Classifier

2020 ◽  
Vol 6 (2) ◽  
pp. 20-26
Author(s):  
Amreen Khan ◽  
Dr. Abhishek Bhatt
Keyword(s):  
Random Forest ◽  
High Performance ◽  
Research Work ◽  
Personal Data ◽  
Recognition System ◽  
Identification System ◽  
Optimum Approach ◽  
Vein Recognition ◽  
Effective Models ◽  
Result Analysis

In recent years, the need for security of personal data is becoming progressively important. A biometric system is an evolving technology that is used in various fields like forensics, secured area and security system. With respect to this concern, the identification system based on the fusion of multibiometric values is the most recommended in order to significantly improve and obtain high performance accuracy. The main purpose of this research work is to design and propose a hybrid system of combining the effect of three effective models: Retinex Algorithm, Stacked Deep Auto Encoder and Random forest (RF) classifier based on multi-biometric fingerprint as well as finger-vein recognition system. According to literature several fingerprint as well as fingervein 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. In order to gain above mentioned objectives, fingerprint and fingervein dataset is taken for training and testing. The result analysis shows approx. 97% accuracy, 92% precision rate as well as 0.04 EER that shows enhancement over existing work.

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 Person Verification Based on Multimodal Biometric Recognition

2021 ◽  
Vol 30 (1) ◽  
pp. 161-183
Author(s):  
Annie Anak Joseph ◽  
Alex Ng Ho Lian ◽  
Kuryati Kipli ◽  
Kho Lee Chin ◽  
Dayang Azra Awang Mat ◽  
...  
Keyword(s):  
Promising Result ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Biometric System ◽  
Score Level Fusion ◽  
Person Recognition ◽  
Biometric Systems ◽  
Verification Process ◽  
Multimodal Biometric Recognition ◽  
Person Verification

Nowadays, person recognition has received significant attention due to broad applications in the security system. However, most person recognition systems are implemented based on unimodal biometrics such as face recognition or voice recognition. Biometric systems that adopted unimodal have limitations, mainly when the data contains outliers and corrupted datasets. Multimodal biometric systems grab researchers’ consideration due to their superiority, such as better security than the unimodal biometric system and outstanding recognition efficiency. Therefore, the multimodal biometric system based on face and fingerprint recognition is developed in this paper. First, the multimodal biometric person recognition system is developed based on Convolutional Neural Network (CNN) and ORB (Oriented FAST and Rotated BRIEF) algorithm. Next, two features are fused by using match score level fusion based on Weighted Sum-Rule. The verification process is matched if the fusion score is greater than the pre-set threshold. The algorithm is extensively evaluated on UCI Machine Learning Repository Database datasets, including one real dataset with state-of-the-art approaches. The proposed method achieves a promising result in the person recognition system.

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.

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

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

Intelligent recognition system for viewpoint variations on gait and speech using CNN-CapsNet

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
G. Merlin Linda ◽  
N.V.S. Sree Rathna Lakshmi ◽  
N. Senthil Murugan ◽  
Rajendra Prasad Mahapatra ◽  
V. Muthukumaran ◽  
...  
Keyword(s):  
Spatial Information ◽  
Intelligent System ◽  
Gait Recognition ◽  
Research Work ◽  
Recognition System ◽  
Multimodal Fusion ◽  
Content Type ◽  
Surveillance Videos ◽  
Sensor Devices ◽  
Intelligent Recognition

PurposeThe paper aims to introduce an intelligent recognition system for viewpoint variations of gait and speech. It proposes a convolutional neural network-based capsule network (CNN-CapsNet) model and outlining the performance of the system in recognition of gait and speech variations. The proposed intelligent system mainly focuses on relative spatial hierarchies between gait features in the entities of the image due to translational invariances in sub-sampling and speech variations.Design/methodology/approachThis proposed work CNN-CapsNet is mainly used for automatic learning of feature representations based on CNN and used capsule vectors as neurons to encode all the spatial information of an image by adapting equal variances to change in viewpoint. The proposed study will resolve the discrepancies caused by cofactors and gait recognition between opinions based on a model of CNN-CapsNet.FindingsThis research work provides recognition of signal, biometric-based gait recognition and sound/speech analysis. Empirical evaluations are conducted on three aspects of scenarios, namely fixed-view, cross-view and multi-view conditions. The main parameters for recognition of gait are speed, change in clothes, subjects walking with carrying object and intensity of light.Research limitations/implicationsThe proposed CNN-CapsNet has some limitations when considering for detecting the walking targets from surveillance videos considering multimodal fusion approaches using hardware sensor devices. It can also act as a pre-requisite tool to analyze, identify, detect and verify the malware practices.Practical implicationsThis research work includes for detecting the walking targets from surveillance videos considering multimodal fusion approaches using hardware sensor devices. It can also act as a pre-requisite tool to analyze, identify, detect and verify the malware practices.Originality/valueThis proposed research work proves to be performing better for the recognition of gait and speech when compared with other techniques.

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