Multimodal Biometric-Based Authentication with Secured Templates

2020 ◽  
pp. 2150018
Author(s):  
Swati K. Choudhary ◽  
Ameya K. Naik
Keyword(s):  
Discrete Wavelet ◽  
Memory Requirement ◽  
Template Security ◽  
Face Features ◽  
Positive Balance ◽  
Biometric Systems ◽  
Memory Resource ◽  
The Cost ◽  
Biometric Templates ◽  
Biometric Based Authentication

This paper proposes a multimodal biometric based authentication (verification and identification) with secured templates. Multimodal biometric systems provide improved authentication rate over unimodal systems at the cost of increased concern for memory requirement and template security. The proposed framework performs person authentication using face and fingerprint. Biometric templates are protected by hiding fingerprint into face at secret locations, through blind and key-based watermarking. Face features are extracted from approximation sub-band of Discrete Wavelet Transform, which reduces the overall working plane. The proposed method also shows high robustness of biometric templates against common channel attacks. Verification and identification performances are evaluated using two chimeric and one real multimodal dataset. The same systems, working with compressed templates provides considerable reduction in overall memory requirement with negligible loss of authentication accuracies. Thus, the proposed framework offers positive balance between authentication performance, template robustness and memory resource utilization.

scholarly journals Discrete Transforms and Matrix Rotation Based Cancelable Face and Fingerprint Recognition for Biometric Security Applications

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1361
Author(s):  
Abeer D. Algarni ◽  
Ghada El Banby ◽  
Sahar Ismail ◽  
Walid El-Shafai ◽  
Fathi E. Abd El-Samie ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Fractional Fourier Transform ◽  
Fingerprint Recognition ◽  
Discrete Wavelet ◽  
Security Level ◽  
Cancelable Biometrics ◽  
Security Applications ◽  
Discrete Transforms ◽  
Transform Domains ◽  
Biometric Templates

The security of information is necessary for the success of any system. So, there is a need to have a robust mechanism to ensure the verification of any person before allowing him to access the stored data. So, for purposes of increasing the security level and privacy of users against attacks, cancelable biometrics can be utilized. The principal objective of cancelable biometrics is to generate new distorted biometric templates to be stored in biometric databases instead of the original ones. This paper presents effective methods based on different discrete transforms, such as Discrete Fourier Transform (DFT), Fractional Fourier Transform (FrFT), Discrete Cosine Transform (DCT), and Discrete Wavelet Transform (DWT), in addition to matrix rotation to generate cancelable biometric templates, in order to meet revocability and prevent the restoration of the original templates from the generated cancelable ones. Rotated versions of the images are generated in either spatial or transform domains and added together to eliminate the ability to recover the original biometric templates. The cancelability performance is evaluated and tested through extensive simulation results for all proposed methods on a different face and fingerprint datasets. Low Equal Error Rate (EER) values with high AROC values reflect the efficiency of the proposed methods, especially those dependent on DCT and DFrFT. Moreover, a comparative study is performed to evaluate the proposed method with all transformations to select the best one from the security perspective. Furthermore, a comparative analysis is carried out to test the performance of the proposed schemes with the existing schemes. The obtained outcomes reveal the efficiency of the proposed cancelable biometric schemes by introducing an average AROC of 0.998, EER of 0.0023, FAR of 0.008, and FRR of 0.003.

scholarly journals Multimodal Biometric Recognition Using Iris and Face Features

2021 ◽  
Author(s):  
Sulaiman Alshebli ◽  
Fatih Kurugollu ◽  
Mahmoud Shafik
Keyword(s):  
Iris Recognition ◽  
Evaluation Process ◽  
Research Programme ◽  
Discrete Wavelet ◽  
Ongoing Research ◽  
Face Features ◽  
New Feature ◽  
Multimodal Biometric Recognition ◽  
Value Decomposition ◽  
Ongoing Research Programme

Multimodal biometrics has recently gained interest over single biometric modalities. This interest stems from the fact that this technique offers improvements in recognition and more security. In this ongoing research programme, we propose a new feature extraction technique for a biometric system based on face and iris recognition. The extraction of iris and facial features is performed using the Discrete Wavelet Transform combined with the Singular Value Decomposition. Merging the relevant characteristics of the two modalities is used to create a pattern for each individual in the dataset. The evaluation process is performed using two datasets (i.e., Faces94 Faces dataset and IIT Delhi Iris dataset). The experimental results carried out in this programme showed the robustness of the proposed technique.

scholarly journals GASTRO-CADx: a three stages framework for diagnosing gastrointestinal diseases

2021 ◽  
Vol 7 ◽  
pp. e423
Author(s):  
Omneya Attallah ◽  
Maha Sharkas
Keyword(s):  
Temporal Frequency ◽  
Gastrointestinal Diseases ◽  
Feature Reduction ◽  
Inspection Time ◽  
Discrete Wavelet ◽  
Spatial Features ◽  
Third Stage ◽  
The Cost ◽  
Aided Diagnosis

Gastrointestinal (GI) diseases are common illnesses that affect the GI tract. Diagnosing these GI diseases is quite expensive, complicated, and challenging. A computer-aided diagnosis (CADx) system based on deep learning (DL) techniques could considerably lower the examination cost processes and increase the speed and quality of diagnosis. Therefore, this article proposes a CADx system called Gastro-CADx to classify several GI diseases using DL techniques. Gastro-CADx involves three progressive stages. Initially, four different CNNs are used as feature extractors to extract spatial features. Most of the related work based on DL approaches extracted spatial features only. However, in the following phase of Gastro-CADx, features extracted in the first stage are applied to the discrete wavelet transform (DWT) and the discrete cosine transform (DCT). DCT and DWT are used to extract temporal-frequency and spatial-frequency features. Additionally, a feature reduction procedure is performed in this stage. Finally, in the third stage of the Gastro-CADx, several combinations of features are fused in a concatenated manner to inspect the effect of feature combination on the output results of the CADx and select the best-fused feature set. Two datasets referred to as Dataset I and II are utilized to evaluate the performance of Gastro-CADx. Results indicated that Gastro-CADx has achieved an accuracy of 97.3% and 99.7% for Dataset I and II respectively. The results were compared with recent related works. The comparison showed that the proposed approach is capable of classifying GI diseases with higher accuracy compared to other work. Thus, it can be used to reduce medical complications, death-rates, in addition to the cost of treatment. It can also help gastroenterologists in producing more accurate diagnosis while lowering inspection time.

scholarly journals A New Method for Gait Recognition Using 2D Poses

2020 ◽  
Author(s):  
Daniel Jangua ◽  
Aparecido Marana
Keyword(s):  
Pose Estimation ◽  
Gait Recognition ◽  
Recognition Rate ◽  
New Method ◽  
Chi Square ◽  
Computational Performance ◽  
Face Features ◽  
Gait Biometrics ◽  
Biometric Systems ◽  
Important Challenge

Over the last decades, biometrics has become an important way for human identification in many areas, since it can avoid frauds and increase the security of individuals in society. Nowadays, most popular biometric systems are based on fingerprint and face features. Despite the great development observed in Biometrics, an important challenge lasts, which is the automatic people identification in low-resolution videos captured in unconstrained scenarios, at a distance, in a covert and noninvasive way, with little or none subject cooperation. In these cases, gait biometrics can be the only choice. The goal of this work is to propose a new method for gait recognition using information extracted from 2D poses estimated over video sequences. For 2D pose estimation, our method uses OpenPose, an open-source robust pose estimator, capable of real-time multi-person detection and pose estimation with high accuracy and a good computational performance. In order to assess the new proposed method, we used two public gait datasets, CASIA Gait Dataset-A and CASIA Gait Dataset-B. Both datasets have videos of a number of people walking in different directions and conditions. In our new method, the classification is carried out by a 1-NN classifier. The best results were obtained by using the chi-square distance function, which obtained 95.00% of rank-1 recognition rate on CASIA Gait Dataset-A and 94.22% of rank-1 recognition rate on CASIA Gait Dataset-B, which are comparable to state-of-the-art results.

scholarly journals Lifting-Based Fractional Wavelet Filter: Energy-Efficient DWT Architecture for Low-Cost Wearable Sensors

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Mohd Tausif ◽  
Ekram Khan ◽  
Mohd Hasan ◽  
Martin Reisslein
Keyword(s):  
Energy Efficient ◽  
State Of The Art ◽  
Critical Path ◽  
Low Cost ◽  
Wearable Sensors ◽  
Discrete Wavelet ◽  
Wavelet Filter ◽  
Path Delay ◽  
Memory Requirement ◽  
Critical Path Delay

This paper proposes and evaluates the LFrWF, a novel lifting-based architecture to compute the discrete wavelet transform (DWT) of images using the fractional wavelet filter (FrWF). In order to reduce the memory requirement of the proposed architecture, only one image line is read into a buffer at a time. Aside from an LFrWF version with multipliers, i.e., the LFr WF m , we develop a multiplier-less LFrWF version, i.e., the LFr WF ml , which reduces the critical path delay (CPD) to the delay T a of an adder. The proposed LFr WF m and LFr WF ml architectures are compared in terms of the required adders, multipliers, memory, and critical path delay with state-of-the-art DWT architectures. Moreover, the proposed LFr WF m and LFr WF ml architectures, along with the state-of-the-art FrWF architectures (with multipliers (Fr WF m ) and without multipliers (Fr WF ml )) are compared through implementation on the same FPGA board. The LFr WF m requires 22% less look-up tables (LUT), 34% less flip-flops (FF), and 50% less compute cycles (CC) and consumes 65% less energy than the Fr WF m . Also, the proposed LFr WF ml architecture requires 50% less CC and consumes 43% less energy than the Fr WF ml . Thus, the proposed LFr WF m and LFr WF ml architectures appear suitable for computing the DWT of images on wearable sensors.

Organization of chemotherapeutic preparations in advance: Do we save or waste money?

2020 ◽  
pp. 107815522096798
Author(s):  
Romain-Pacôme Desmaris ◽  
Elisabeth Bermudez ◽  
Maxime Annereau ◽  
François Lemare ◽  
Florian Slimano
Keyword(s):  
Prospective Study ◽  
Data Sources ◽  
Day Hospital ◽  
Drug Budget ◽  
Positive Balance ◽  
Report Data ◽  
A Prospective Study ◽  
Extemporaneous Preparation ◽  
The Cost

Objective The development of oncology day-hospital activities contributes to increase quality of life of patients and consequently have changed their perception about waiting. The extemporaneous preparation of antineoplastic has become difficult to achieve given the increasing activity, and hospital pharmacists have taken up the challenge by the implementation of the antineoplastic preparation in anticipation. Because anticipation can lead to an important number of preparations to be discarded, we also develop a recycled process for other patients to limit these waste extra costs. We aim to demonstrate the positive balance of anticipated preparation in this 4-year study report. Data sources: This prospective study was conducted in a major European oncology day-hospital from January, 2012 to December, 2015. The data were extracted from our software WinSimbad™ and updated as needed. The number and cost-associated of preparation ungiven chemotherapy doses (recycled or discarded) were compared to the global drug budget of our hospital in order to not exceed 2%. Data summary: 303,100 antineoplastic have been prepared. Approximately 35% of them were anticipated with an average of 5,431±984 that were finally ungiven. Two-third was recycled and the cost of the ungiven preparations finally discarded represents 1.7±0.15% of the global drug budget. Conclusions This study assesses the drug wastage and its associated cost of this concept through a prospective study and discusses the cost of ungiven antineoplastic preparations. With prior consideration of the need to define the acceptable rate of discarded ungiven preparation, the hospitals with an high oncology day-hospital activity should implement this approach.

scholarly journals Palmprint False Acceptance Attack with a Generative Adversarial Network (GAN)

2020 ◽  
Vol 10 (23) ◽  
pp. 8547
Author(s):  
Fei Wang ◽  
Lu Leng ◽  
Andrew Beng Jin Teoh ◽  
Jun Chu
Keyword(s):  
Multimedia Systems ◽  
Generative Adversarial Networks ◽  
Clustering Method ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Biometric Systems ◽  
Biometric Based Authentication ◽  
False Acceptance

Biometric-based authentication is widely deployed on multimedia systems currently; however, biometric systems are vulnerable to image-level attacks for impersonation. Reconstruction attack (RA) and presentation attack (PA) are two typical instances for image-level attacks. In RA, the reconstructed images often have insufficient naturalness due to the presence of remarkable counterfeit appearance, thus their forgeries can be easily detected by machine or human. The PA requires genuine users’ original images, which are difficult to acquire in practice and to counterfeit fake biometric images on spoofing carriers. In this paper, we develop false acceptance attack (FAA) for a palmprint biometric, which overcomes the aforementioned problems of RA and PA. FAA does not require genuine users’ images, and it can be launched simply with the synthetic images with high naturalness, which are generated by the generative adversarial networks. As a case study, we demonstrate the feasibility of FAA against coding-based palmprint biometric systems. To further improve the efficiency of FAA, we employ a clustering method to select diverse fake images in order to enhance the diversity of the fake images used, so the number of attack times is reduced. Our experimental results show the success rate and effectiveness of the FAA.

A Secure Framework to Preserve Privacy of Biometric Templates on Cloud using Deep Learning

2020 ◽  
Vol 13 ◽  
Author(s):  
Shefali Arora ◽  
MPS Bhatia
Keyword(s):  
Cloud Computing ◽  
Deep Convolutional Neural Networks ◽  
Biometric Data ◽  
Template Security ◽  
Biometric Template ◽  
Matching Performance ◽  
Remote Services ◽  
Security And Reliability ◽  
Biometric Templates ◽  
Secure Framework

Introduction: Cloud computing involves the use of maximum remote services through a network using minimum resources via internet. There are various issues associated with cloud computing, such as privacy, security and reliability. Due to rapidly increasing information on the cloud, it is important to ensure security of user information. Biometric template security over cloud is one such concern. Leakage of unprotected biometric data can serve as a major risk for the privacy of individuals and security of real-world applications. Method: In this paper, we improvise a secure framework named DeepCrypt, that can be applied to protect biometric templates during authentication of biometric templates. We use deep Convolutional Neural Networks to extract features from these modalities. The resulting features are hashed using a secure combination of Blowcrypt (Bcrypt) and SHA-256 algorithm, which salts the templates by default before storing on the server. Results: Experiments conducted on the CASIA-Iris-M1-S1, CMU-PIE and FVC-2006 datasets achieve around 99% Genuine accept rates, proving that this technique helps to achieve better performance along with high template security. Discussion: The proposed method is robust and provides cancellable biometric templates, high security and better matching performance as compared to traditional techniques used to protect biometric template.

Privacy Enhancing Technologies in Biometrics

2010 ◽  
pp. 1-22
Author(s):  
Patrizio Campisi ◽  
Emanuele Maiorana ◽  
Alessandro Neri
Keyword(s):  
Best Practices ◽  
State Of The Art ◽  
Personal Information ◽  
Privacy Requirements ◽  
Unique Information ◽  
Personal Attitudes ◽  
Privacy Enhancing Technologies ◽  
Biometric Templates ◽  
Biometric Based Authentication

The wide diffusion of biometric based authentication systems, which has been witnessed in the last few years, has raised the need to protect both the security and the privacy of the employed biometric templates. In fact, unlike passwords or tokens, biometric traits cannot be revoked or reissued and, if compromised, they can disclose unique information about the user’s identity. Moreover, since biometrics represent personal information, they can be used to acquire data which can be used to discriminate people because of religion, health, sex, gender, personal attitudes, and so forth. In this chapter, the privacy requirements, the major threats to privacy, and the best practices to employ in order to deploy privacy sympathetic systems, are discussed within the biometric framework. An overview of state of the art on privacy enhancing technologies, applied to biometric based authentication systems, is presented.

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