scholarly journals New Authentication Model for Multimodal Biometrics Based on Shape Features Vectors

2019 ◽  
Vol 54 (6) ◽  
Author(s):  
Sundos Abdulameer Alazawi ◽  
Huda Abdulaaliabdulbaqi ◽  
Yasmin Makki Mohialden
Keyword(s):  
High Performance ◽  
Feature Fusion ◽  
Personal Data ◽  
Rejection Rate ◽  
Shape Features ◽  
Multimodal Biometrics ◽  
Invariant Moments ◽  
Measurement And Analysis ◽  
Authentication System ◽  
Form Features

Biometrics is the science and technology dealing with the measurement and analysis of the biological features of the human body. The analysis is based on comparing the value of certain measured features with the form features in the database. Unimodal Biometric Systems have many limitations regarding precision in the identification/authentication of personal data. To accurately identify a person, a multimodal biometrics system such as combining face and fingerprint characteristic is used. Many such multi-biometrics fusion possibilities exist that can be utilized as an authentication system. In this paper, we present a new authentication system of the multimodal biometrics method for both face and fingerprint characteristics based on general shape feature fusion vectors. There are two main phases in our method: first, the fused shape features for both face and fingerprint images are extracted in accordance with central moments, and second, these features were recognized for retrieval of an authorized person using direct Euclidian distance. Experimentally, we tested about 100 shape features vectors, and observed that our method allows to improve the multimodal biometrics model when we are using the same features for two biometric images. A new method has a high-performance precision when invariant moments are used to extract shape features vectors and when similarity measurements computed based on direct Euclidean distance in the experiments are performed. We recorded False Acceptance Rate, False Rejection Rate, and Accuracy, FAR and FRR where the accuracy of the model is 91 %.

scholarly journals A Fast and Lightweight Method with Feature Fusion and Multi-Context for Face Detection

2018 ◽  
Vol 10 (8) ◽  
pp. 80
Author(s):  
Lei Zhang ◽  
Xiaoli Zhi
Keyword(s):  
Face Detection ◽  
Graphics Processing Units ◽  
High Performance ◽  
Feature Fusion ◽  
Local Context ◽  
Data Set ◽  
Global Context ◽  
Detection Algorithms ◽  
Multi Scale ◽  
Benchmark Datasets

Convolutional neural networks (CNN for short) have made great progress in face detection. They mostly take computation intensive networks as the backbone in order to obtain high precision, and they cannot get a good detection speed without the support of high-performance GPUs (Graphics Processing Units). This limits CNN-based face detection algorithms in real applications, especially in some speed dependent ones. To alleviate this problem, we propose a lightweight face detector in this paper, which takes a fast residual network as backbone. Our method can run fast even on cheap and ordinary GPUs. To guarantee its detection precision, multi-scale features and multi-context are fully exploited in efficient ways. Specifically, feature fusion is used to obtain semantic strongly multi-scale features firstly. Then multi-context including both local and global context is added to these multi-scale features without extra computational burden. The local context is added through a depthwise separable convolution based approach, and the global context by a simple global average pooling way. Experimental results show that our method can run at about 110 fps on VGA (Video Graphics Array)-resolution images, while still maintaining competitive precision on WIDER FACE and FDDB (Face Detection Data Set and Benchmark) datasets as compared with its state-of-the-art counterparts.

An optical authentication system based on encryption technique and multimodal biometrics

2013 ◽  
Vol 54 ◽  
pp. 120-127 ◽  
Author(s):  
Sheng Yuan ◽  
Tong Zhang ◽  
Xin Zhou ◽  
Xuemei Liu ◽  
Mingtang Liu
Keyword(s):  
Multimodal Biometrics ◽  
Authentication System

scholarly journals Fabrication of High-Performance Thin-Film Composite Nanofiltration Membrane by Dynamic Calcium-Carboxyl Intra-Bridging during Post-Treatment

Membranes ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 137
Author(s):  
Hongyi Han ◽  
Ruobin Dai ◽  
Zhiwei Wang
Keyword(s):  
Thin Film ◽  
Water Purification ◽  
High Performance ◽  
Rejection Rate ◽  
Post Treatment ◽  
Nanofiltration Membrane ◽  
Film Composite ◽  
Thin Film Composite ◽  
Post Modification

Widespread applications of nanofiltration (NF) and reverse osmosis (RO)-based processes for water purification and desalination call for high-performance thin-film composite (TFC) membranes. In this work, a novel and facile modification method was proposed to fabricate high-performance thin-film composite nanofiltration membrane by introducing Ca2+ in the heat post-treatment. The introduction of Ca2+ induced in situ Ca2+-carboxyl intra-bridging, leading to the embedment of Ca2+ in the polyamide (PA) layer. This post modification enhanced the hydrophilicity and surface charge of NF membranes compared to the pristine membrane. More interestingly, the modified membrane had more nodules and exhibited rougher morphology. Such changes brought by the addition of Ca2+ enabled the significant increase of water permeability (increasing from 17.9 L·m−2·h−1·bar−1 to 29.8 L·m−2·h−1·bar−1) while maintaining a high selectivity (Na2SO4 rejection rate of 98.0%). Furthermore, the intra-bridging between calcium and carboxyl imparted the NF membranes with evident antifouling properties, exhibiting milder permeability decline of 4.2% (compared to 16.7% of NF-control) during filtration of sodium alginate solution. The results highlight the potential of using Ca2+-carboxyl intra-bridging post-treatment to fabricate high-performance TFC membranes for water purification and desalination.

scholarly journals Multimodal biometrics of fingerprint and signature recognition using multi-level feature fusion and deep learning techniques

2021 ◽  
Vol 22 (1) ◽  
pp. 187
Author(s):  
Arjun Benagatte Channegowda ◽  
H N Prakash
Keyword(s):  
Deep Learning ◽  
Feature Fusion ◽  
Research Work ◽  
Recognition Rate ◽  
Recognition System ◽  
Multimodal Biometrics ◽  
Histograms Of Oriented Gradients ◽  
Multi Level ◽  
Deep Learning Neural Network ◽  
Hidden Neurons

Providing security in biometrics is the major challenging task in the current situation. A lot of research work is going on in this area. Security can be more tightened by using complex security systems, like by using more than one biometric trait for recognition. In this paper multimodal biometric models are developed to improve the recognition rate of a person. The combination of physiological and behavioral biometrics characteristics is used in this work. Fingerprint and signature biometrics characteristics are used to develop a multimodal recognition system. Histograms of oriented gradients (HOG) features are extracted from biometric traits and for these feature fusions are applied at two levels. Features of fingerprint and signatures are fused using concatenation, sum, max, min, and product rule at multilevel stages, these features are used to train deep learning neural network model. In the proposed work, multi-level feature fusion for multimodal biometrics with a deep learning classifier is used and results are analyzed by a varying number of hidden neurons and hidden layers. Experiments are carried out on SDUMLA-HMT, machine learning and data mining lab, Shandong University fingerprint datasets, and MCYT signature biometric recognition group datasets, and encouraging results were obtained.

Advances in Biometrics for Secure Human Authentication System

Biometrics ◽  
2017 ◽  
pp. 1834-1852
Author(s):  
Jagannath Mohan ◽  
Adalarasu Kanagasabai ◽  
Vetrivelan Pandu
Keyword(s):  
Recent Decade ◽  
Biometric Identification ◽  
Multimodal Biometrics ◽  
Biometric Analysis ◽  
Multifactor Authentication ◽  
Potential Alternative ◽  
Authentication System ◽  
Identification Techniques ◽  
Electrocardiogram Ecg ◽  
Pcg Signals

In the recent decade, one of our major concerns in the global technological society of information security is confirmation that a person accessing confidential information is authorized to perform so. Such mode of access is generally accomplished by a person's confirming their identity by the use of some method of authentication system. In present days, the requirement for safe security in storing individual information has been developing rapidly and among the potential alternative is implementing innovative biometric identification techniques. This chapter discusses how the advent of the 20th century has brought forth the security principles of identification and authentication in the field of biometric analysis. The chapter reviews vulnerabilities in biometric authentication and issues in system implementation. The chapter also proposes the multifactor authentication and the use of multimodal biometrics, i.e., the combination of Electrocardiogram (ECG) and Phonocardiogram (PCG) signals to enhance reliability in the authentication process.

scholarly journals Data Improvement Model Based on ECG Biometric for User Authentication and Identification

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2920 ◽  
Author(s):  
Alex Barros ◽  
Paulo Resque ◽  
João Almeida ◽  
Renato Mota ◽  
Helder Oliveira ◽  
...  
Keyword(s):  
User Authentication ◽  
Personal Data ◽  
Large Data ◽  
Real Data ◽  
Rejection Rate ◽  
Security Requirements ◽  
Biometric Identification ◽  
Data Set ◽  
Rapid Spread ◽  
Data Improvement

The rapid spread of wearable technologies has motivated the collection of a variety of signals, such as pulse rate, electrocardiogram (ECG), electroencephalogram (EEG), and others. As those devices are used to do so many tasks and store a significant amount of personal data, the concern of how our data can be exposed starts to gain attention as the wearable devices can become an attack vector or a security breach. In this context, biometric also has expanded its use to meet new security requirements of authentication demanded by online applications, and it has been used in identification systems by a large number of people. Existing works on ECG for user authentication do not consider a population size close to a real application. Finding real data that has a big number of people ECG’s data is a challenge. This work investigates a set of steps that can improve the results when working with a higher number of target classes in a biometric identification scenario. These steps, such as increasing the number of examples, removing outliers, and including a few additional features, are proven to increase the performance in a large data set. We propose a data improvement model for ECG biometric identification (user identification based on electrocardiogram—DETECT), which improves the performance of the biometric system considering a greater number of subjects, which is closer to a security system in the real world. The DETECT model increases precision from 78% to 92% within 1500 subjects, and from 90% to 95% within 100 subjects. Moreover, good False Rejection Rate (i.e., 0.064003) and False Acceptance Rate (i.e., 0.000033) were demonstrated. We designed our proposed method over PhysioNet Computing in Cardiology 2018 database.

scholarly journals Development of Retrieval System using Color from Multi-Segments and Shape features from Images as Combination

2019 ◽  
Vol 9 (1) ◽  
pp. 4410-4417
Keyword(s):  
High Performance ◽  
Retrieval System ◽  
Large Data ◽  
Image Features ◽  
Digital Information ◽  
Shape Features ◽  
Novel Approach ◽  
Hu Moments ◽  
Color Histograms ◽  
Similar Images

The world of digital information has been generous to produce large data. The task of finding similar images in such a vast repository in real-time is very challenging. In this paper, Content Based Retrieval System is proposed to retrieve similar images with a novel approach of combining the image features like color and shape. The work includes extraction of color feature from images by dividing them into five significant portions, which emphasizes on center portion of an image where object may reside. This process is carried out and represented using color histograms. Next, shape features are extracted using Hu - moments for the entire image. Later, by combining these two features, similar image retrieval is done based on a query. Various trials of retrieval have been carried out with different categories and results are tabulated. These results are compared with existing works and shows high performance.

scholarly journals Novel Biometric Fusion System using GA-PSO and ANN

2020 ◽  
Vol 9 (4) ◽  
pp. 3122-3129
Keyword(s):  
Rejection Rate ◽  
Fusion System ◽  
Scale Invariant ◽  
Fusion Strategy ◽  
Bee Colony ◽  
Face Features ◽  
Authentication System ◽  
Nature Inspired Algorithms ◽  
High Level ◽  
Biometric Fusion

Technology advancements have led to the emergence of biometrics as the most relevant future authentication technology. On practical grounds, unimodal biometric authentication systems have inevitable momentous limitations due to varied data quality and noise levels. The paper aims at investigating fusion of face and fingerprint biometric characteristics to achieve a high level personal authentication system. In the fusion strategy face features are extracted using Scale-Invariant Feature Transform (SIFT) algorithm and fingerprint features are extracted using minutiae feature extraction. These extracted features are optimized using nature inspired Genetic Algorithm (GA). The efficiency of the proposed fusion authentication system is enhanced by training and testing the data by applying Artificial Neural Network (ANN). The quality of the proposed design is evaluated against two nature inspired algorithms, namely, Particle Swarm Optimization (PSO)and Artificial Bee Colony (ABC) in terms of False Acceptance Rate (FAR), False Rejection Rate (FRR) and recognition accuracy. Simulation results over a range of image sample from 10 to 100 images have shown that the proposed biometric fusion strategy resulted in FARof 2.89, FAR 0.71and accuracy 97.72%.Experimental evaluation of the proposed system also outperformed the existing biometric fusion system.

A Robust Biometrics System Using Finger Knuckle Print

2018 ◽  
pp. 416-446 ◽  
Author(s):  
Ravinder Kumar
Keyword(s):  
Information Theory ◽  
High Performance ◽  
Image Acquisition ◽  
Ease Of Use ◽  
Discrimination Ability ◽  
High Discrimination ◽  
Authentication System

Among various biometric indicators, hand-based biometrics has been widely used and deployed for last two decades. Hand-based biometrics are very popular because of their higher acceptance among the population because of their ease of use, high performance, less expensive, etc. This chapter presents a new hand-based biometric known as finger-knuckle-print (FKP) for a person authentication system. FKP are the images obtained from the one's fingers phalangeal joints and are characterized by internal skin pattern. Like other biometrics discrimination ability, FKP also has the capability of high discrimination. The proposed system consists of four modules: image acquisition, extraction of ROI, selection and extraction of features, and their matching. New features based on information theory are proposed for matching. The performance of the proposed system is evaluated using experiment performed on a database of 7920 images from 660 different fingers. The efficacy of the proposed system is evaluated in terms of matching rate and compromising results are obtained.

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