Multimodal biometric system using deep learning based on face and finger vein fusion

2021 ◽  
pp. 1-13
Author(s):  
Shikhar Tyagi ◽  
Bhavya Chawla ◽  
Rupav Jain ◽  
Smriti Srivastava
Keyword(s):  
High Performance ◽  
Recognition Accuracy ◽  
Error Rates ◽  
Facial Features ◽  
Biometric System ◽  
Deep Convolutional Neural Networks ◽  
Finger Vein ◽  
Biometric Systems ◽  
Overall Performance ◽  
Recognition Systems

Single biometric modalities like facial features and vein patterns despite being reliable characteristics show limitations that restrict them from offering high performance and robustness. Multimodal biometric systems have gained interest due to their ability to overcome the inherent limitations of the underlying single biometric modalities and generally have been shown to improve the overall performance for identification and recognition purposes. This paper proposes highly accurate and robust multimodal biometric identification as well as recognition systems based on fusion of face and finger vein modalities. The feature extraction for both face and finger vein is carried out by exploiting deep convolutional neural networks. The fusion process involves combining the extracted relevant features from the two modalities at score level. The experimental results over all considered public databases show a significant improvement in terms of identification and recognition accuracy as well as equal error rates.

Recognizing Hand-Printed Letters and Digits Using Backpropagation Learning

1991 ◽  
Vol 3 (2) ◽  
pp. 258-267 ◽  
Author(s):  
Gale L. Martin ◽  
James A. Pittman
Keyword(s):  
Character Recognition ◽  
High Performance ◽  
Network Capacity ◽  
Training Sample ◽  
Error Rates ◽  
Training Set ◽  
Set Size ◽  
Bank Checks ◽  
Recognition Systems ◽  
High Recognition Accuracy

We report on results of training backpropagation nets with samples of hand-printed digits scanned off of bank checks and hand-printed letters interactively entered into a computer through a stylus digitizer. Generalization results are reported as a function of training set size and network capacity. Given a large training set, and a net with sufficient capacity to achieve high performance on the training set, nets typically achieved error rates of 4-5% at a 0% reject rate and 1-2% at a 10% reject rate. The topology and capacity of the system, as measured by the number of connections in the net, have surprisingly little effect on generalization. For those developing hand-printed character recognition systems, these results suggest that a large and representative training sample may be the single, most important factor in achieving high recognition accuracy. Benefits of reducing the number of net connections, other than improving generalization, are discussed.

scholarly journals RELIABILITY INDEX FOR IRIS CODE MATCHING IN IRIS RECOGNITION SYSTEMS

2019 ◽  
Vol XLII-2/W12 ◽  
pp. 197-202
Author(s):  
B. H. Shekar ◽  
S. S. Bhat ◽  
A. Maysuradze
Keyword(s):  
System Reliability ◽  
Reliability Index ◽  
Iris Recognition ◽  
Recognition Accuracy ◽  
Simulation Experiments ◽  
Factors Affecting ◽  
Biometric Systems ◽  
Recognition Systems ◽  
Matching Techniques ◽  
Rejection Rates

<p><strong>Abstract.</strong> Iris code matching is an important stage of iris biometric systems which compares the input iris code with stored patterns of enrolled iris codes and classifies the code into one of classes so that, the claim is accepted or rejected. Several classifier based approaches are proposed by the researchers to improve the recognition accuracy. In this paper, we discuss the factors affecting an iris classifier’s performance and we propose a reliability index for iris matching techniques to quantitatively measure the extent of system reliability, based on false acceptance rate and false rejection rates using Monte Carlo Simulation. Experiments are carried out on benchmark databases such as, IITD, MMU v-2, CASIA v-4 Distance and UBIRIS v.2.</p>

Contemporary Biometric System Design

2020 ◽  
pp. 292-324
Author(s):  
Himanshu Purohit ◽  
Pawan K Ajmera
Keyword(s):  
Artificial Intelligence ◽  
Noisy Data ◽  
Error Rates ◽  
Biometric System ◽  
Behavioral Traits ◽  
Biometric Systems ◽  
Physical Traits ◽  
Multiple Scenarios ◽  
Different Levels ◽  
Multimodal Biometric Systems

Individual's Identity Authentication depends on physical traits like face, iris, and fingerprint, etc., or behavioral traits like voice and signature. With the rapid advancement in the field of biometrics, multimodal biometric systems are replacing unimodal biometric systems. As the application of molecular biometric system removes certain errors like noisy data, interclass variations, spoof attacks, and unacceptable error rates as compared to unimodal biometric systems. Even the possibilities of multiple scenarios present in multimodal biometric systems are quite helpful for the consolidation of information using different levels of fusion. In this chapter, the authors try to analyze the technological change which is present due to growing field of biometrics with artificial intelligence and undergone a thorough research for multimodal biometric systems for effective authentication purpose. This study is quite helpful for getting different perception for the use of biometrics as a highest level of network security due to the fusion of many different modalities.

scholarly journals Security and Accuracy of Fingerprint-Based Biometrics: A Review

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 141 ◽  
Author(s):  
Wencheng Yang ◽  
Song Wang ◽  
Jiankun Hu ◽  
Guanglou Zheng ◽  
Craig Valli
Keyword(s):  
Recognition Accuracy ◽  
Research Work ◽  
Research Topic ◽  
Foreseeable Future ◽  
Biometric System ◽  
Systems Security ◽  
Biometric Systems ◽  
High Recognition Accuracy ◽  
Future Work ◽  
Shed Light

Biometric systems are increasingly replacing traditional password- and token-based authentication systems. Security and recognition accuracy are the two most important aspects to consider in designing a biometric system. In this paper, a comprehensive review is presented to shed light on the latest developments in the study of fingerprint-based biometrics covering these two aspects with a view to improving system security and recognition accuracy. Based on a thorough analysis and discussion, limitations of existing research work are outlined and suggestions for future work are provided. It is shown in the paper that researchers continue to face challenges in tackling the two most critical attacks to biometric systems, namely, attacks to the user interface and template databases. How to design proper countermeasures to thwart these attacks, thereby providing strong security and yet at the same time maintaining high recognition accuracy, is a hot research topic currently, as well as in the foreseeable future. Moreover, recognition accuracy under non-ideal conditions is more likely to be unsatisfactory and thus needs particular attention in biometric system design. Related challenges and current research trends are also outlined in this paper.

scholarly journals Presentation Attack Detection on Limited-Resource Devices Using Deep Neural Classifiers Trained on Consistent Spectrogram Fragments

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7728
Author(s):  
Kacper Kubicki ◽  
Paweł Kapusta ◽  
Krzysztof Ślot
Keyword(s):  
English Language ◽  
Recognition Accuracy ◽  
Speaker Verification ◽  
Recognition Task ◽  
Error Rates ◽  
Attack Detection ◽  
Deep Convolutional Neural Networks ◽  
Phoneme Recognition ◽  
Resource Limited ◽  
Presentation Attack Detection

The presented paper is concerned with detection of presentation attacks against unsupervised remote biometric speaker verification, using a well-known challenge–response scheme. We propose a novel approach to convolutional phoneme classifier training, which ensures high phoneme recognition accuracy even for significantly simplified network architectures, thus enabling efficient utterance verification on resource-limited hardware, such as mobile phones or embedded devices. We consider Deep Convolutional Neural Networks operating on windows of speech Mel-Spectrograms as a means for phoneme recognition, and we show that one can boost the performance of highly simplified neural architectures by modifying the principle underlying training set construction. Instead of generating training examples by slicing spectrograms using a sliding window, as it is commonly done, we propose to maximize the consistency of phoneme-related spectrogram structures that are to be learned, by choosing only spectrogram chunks from the central regions of phoneme articulation intervals. This approach enables better utilization of the limited capacity of the considered simplified networks, as it significantly reduces a within-class data scatter. We show that neural architectures comprising as few as dozens of thousands parameters can successfully—with accuracy of up to 76%, solve the 39-phoneme recognition task (we use the English language TIMIT database for experimental verification of the method). We also show that ensembling of simple classifiers, using a basic bagging method, boosts the recognition accuracy by another 2–3%, offering Phoneme Error Rates at the level of 23%, which approaches the accuracy of the state-of-the-art deep neural architectures that are one to two orders of magnitude more complex than the proposed solution. This, in turn, enables executing reliable presentation attack detection, based on just few-syllable long challenges on highly resource-limited computing hardware.

scholarly journals Impact on Biometric Identification Systems of COVID-19

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yuheng Guo
Keyword(s):  
Face Recognition ◽  
Comprehensive Evaluation ◽  
Biometric Identification ◽  
Biometric System ◽  
Biometric Systems ◽  
Face Detection And Recognition ◽  
Considerable Impact ◽  
Recognition Systems ◽  
The Impact ◽  
Detection And Recognition

COVID-19 has had an inevitable impact on the daily life of people in 2020. Changes in behavior such as wearing masks have a considerable impact on biometric systems, especially face recognition systems. When people are aware of this impact, a comprehensive evaluation of this phenomenon is lacking. The purpose of this paper is to qualitatively evaluate the impact of COVID-19 on various biometric systems and to quantitatively evaluate face detection and recognition. The experimental results show that a real-world masked face dataset is essential to build an effective face recognition-based biometric system.

scholarly journals The Effects of Facial Expressions on Face Biometric System’s Reliability

Information ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 485
Author(s):  
Hind A. Alrubaish ◽  
Rachid Zagrouba
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Rejection Rate ◽  
Facial Features ◽  
Biometric System ◽  
Acceptance Threshold ◽  
Face Shape ◽  
Biometric Systems ◽  
The Face ◽  
Face Biometric

The human mood has a temporary effect on the face shape due to the movement of its muscles. Happiness, sadness, fear, anger, and other emotional conditions may affect the face biometric system’s reliability. Most of the current studies on facial expressions are concerned about the accuracy of classifying the subjects based on their expressions. This study investigated the effect of facial expressions on the reliability of a face biometric system to find out which facial expression puts the biometric system at greater risk. Moreover, it identified a set of facial features that have the lowest facial deformation caused by facial expressions to be generalized during the recognition process, regardless of which facial expression is presented. In order to achieve the goal of this study, an analysis of 22 facial features between the normal face and six universal facial expressions is obtained. The results show that the face biometric systems are affected by facial expressions where the disgust expression achieved the most dissimilar score, while the sad expression achieved the lowest dissimilar score. Additionally, the study identified the five and top ten facial features that have the lowest facial deformations on the face shape in all facial expressions. Besides that, the relativity score showed less variances between the sample using the top facial features. The obtained results of this study minimized the false rejection rate in the face biometric system and subsequently the ability to raise the system’s acceptance threshold to maximize the intrusion detection rate without affecting the user convenience.

Graphs in Biometrics

2011 ◽  
pp. 151-180
Author(s):  
Dakshina Ranjan Kisku ◽  
Phalguni Gupta ◽  
Jamuna Kanta Sing
Keyword(s):  
Real Time ◽  
Graph Matching ◽  
Image Plane ◽  
Individual Identification ◽  
Behavioral Characteristics ◽  
Biometric System ◽  
Biometric Systems ◽  
Pattern Recognition Systems ◽  
Recognition Systems ◽  
Matching Techniques

Biometric systems are considered as human pattern recognition systems that can be used for individual identification and verification. The decision on the authenticity is done with the help of some specific measurable physiological or behavioral characteristics possessed by the individuals. Robust architecture of any biometric system provides very good performance of the system against rotation, translation, scaling effect and deformation of the image on the image plane. Further, there is a need of development of real-time biometric system. There exist many graph matching techniques used to design robust and real-time biometrics systems. This chapter discusses different types of graph matching techniques that have been successfully used in different biometric traits.

scholarly journals A State of Art for Survey of Combined Iris and Fingerprint Recognition Systems

2021 ◽  
pp. 18-33
Author(s):  
Dindar Mikaeel Ahmed ◽  
Siddeeq Y. Ameen ◽  
Naaman Omar ◽  
Shakir Fattah Kak ◽  
Zryan Najat Rashid ◽  
...  
Keyword(s):  
Fingerprint Recognition ◽  
Facial Features ◽  
Biometric System ◽  
Data Set ◽  
Identification Methods ◽  
Recognition Systems ◽  
Technological Science ◽  
Iris Identification ◽  
Biometric Features ◽  
State Of Art

Biometrics is developing into a technological science in this lifelong technology for the defense of identification. Biometrics is the technology to recognize individuals based on facial features, fingerprints, iris, retina, speech, handprints, etc. Biometric features are used for human recognition and identification. Much research was done in the last years on the biometric system because of a growing need for identification methods. This paper offers an overview of biometric solutions using fingerprint and iris identification, their uses, and Compare the data set, methods, Fusion Level, and the accuracy of the results.

scholarly journals Biometric Technologies in Recognition Systems: A Survey

2019 ◽  
Vol 24 (6) ◽  
pp. 132
Author(s):  
Shihab A. Shawkat1 ◽  
Raya N. Ismail2
Keyword(s):  
Identity Management ◽  
Personal Attributes ◽  
Behavioral Characteristics ◽  
Human Society ◽  
Biometric System ◽  
Comprehensive Overview ◽  
Biometric Systems ◽  
Identity Management Systems ◽  
Recognition Systems ◽  
Hand Geometry

The ability to recognize people uniquely and to associate personal attributes such as name and nationality with them has been very important to the fabric of human society. Nowadays, modern societies have an explosion in population growth and increased mobility which necessitated building advanced identity management systems for recording and maintaining people’s identities. In the last decades, biometrics has played an important role in recognizing people instead of traditional ways such as passwords and keys which can be forgotten or be stolen. Biometric systems employ physiological and/or behavioral characteristics of people to verify their identities. There are different biometric modalities that can be used to recognize people such as fingerprints, face, hand geometry, voice, iris, signature, etc. In this paper, a comprehensive overview have been provided on the major issues of biometric systems including general biometric system architecture, major biometric traits, biometric systems performance, and some relevant works.   http://dx.doi.org/10.25130/tjps.24.2019.120

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