Deep user identification model with multiple biometric data

Song, Hyoung-Kyu; AlAlkeem, Ebrahim; Yun, Jae-Woong; Kim, Sang Woo; Yoo, Hyerin; Heo, Dasom; Chae, Myungsu; Yeun, Chan Yeob

doi:10.1186/s12859-020-03613-3

Cited by 12 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the contrary, Song et al [ 281 ] implemented a ResNet-50 architecture for person identification using multiple ECG, face, and fingerprint data from several public and private databases and achieved an accuracy of 98.97% for ID classification and 96.55% for gender classification. Finally, AlDuwaile and Islam [ 283 ] tested several pretrained models, including GoogleNet, ResNet, MobileNet, and EfficientNet, and a CNN model to perform human recognition using ECG signals that originated from 2 public databases and achieved an accuracy between 94.18% and 98.20% for ECG-ID mixed-session and multisession data sets.…”

Section: Resultsmentioning

confidence: 99%

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

Petmezas¹,

Stefanopoulos²,

Kilintzis³

et al. 2022

JMIR Med Inform

View full text Add to dashboard Cite

Background Electrocardiogram (ECG) is one of the most common noninvasive diagnostic tools that can provide useful information regarding a patient’s health status. Deep learning (DL) is an area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals. Objective This study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications. Methods The PubMed search engine was systematically searched by combining “deep learning” and keywords such as “ecg,” “ekg,” “electrocardiogram,” “electrocardiography,” and “electrocardiology.” Irrelevant articles were excluded from the study after screening titles and abstracts, and the remaining articles were further reviewed. The reasons for article exclusion were manuscripts written in any language other than English, absence of ECG data or DL methods involved in the study, and absence of a quantitative evaluation of the proposed approaches. Results We identified 230 relevant articles published between January 2020 and December 2021 and grouped them into 6 distinct medical applications, namely, blood pressure estimation, cardiovascular disease diagnosis, ECG analysis, biometric recognition, sleep analysis, and other clinical analyses. We provide a complete account of the state-of-the-art DL strategies per the field of application, as well as major ECG data sources. We also present open research problems, such as the lack of attempts to address the issue of blood pressure variability in training data sets, and point out potential gaps in the design and implementation of DL models. Conclusions We expect that this review will provide insights into state-of-the-art DL methods applied to ECG data and point to future directions for research on DL to create robust models that can assist medical experts in clinical decision-making.

show abstract

Section: Resultsmentioning

confidence: 99%

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

Petmezas¹,

Stefanopoulos²,

Kilintzis³

et al. 2022

JMIR Med Inform

View full text Add to dashboard Cite

show abstract

“…A hybrid multimodal authentication protocol was presented in [43], wherein face recognition, fingerprint, and ECG data were used to authenticate the user and achieve gender reveal features. The proposed model uses feature extraction for each dataset, as each set can have distinctive characteristics and requires its own cleaning procedure.…”

Section: B User Authenticationmentioning

confidence: 99%

Empirical Studies of TESLA Protocol: Properties, Implementations, and Replacement of Public Cryptography Using Biometric Authentication

et al. 2022

Self Cite

View full text Add to dashboard Cite

This study discusses the general overview of Timed Efficient Stream Loss-tolerant Authentication (TESLA) protocol, including its properties, key setups, and improvement protocols. The discussion includes a new proposed two-level infinite µTESLA (TLI µTESLA) protocol that solves the authentication delay and synchronization issues. We theoretically compared TLI µTESLA with the previously proposed protocols in terms of security services and showed that the new protocol prevents excessive use of the buffer in the sensor node and reduces the DoS attacks on the network. In addition, it accelerates the authentication process of the broadcasted message with less delay and assures continuous receipt of packets compared to previous TESLA Protocols. We also addressed the challenges faced during the implementation of TESLA protocol and presented the recent solutions and parameter choices for improving the efficiency of the TESLA protocol. Moreover, we focused on utilizing biometric authentication as a promising approach to replace public cryptography in the authentication process.INDEX TERMS Biometric authentication, lightweight cryptography, machine learning, TESLA protocol. I. INTRODUCTION TO LIGHTWEIGHT CRYPTOGRAPHYCurrently, the internet of things (IoT) is rapidly expanding and being applied to several fields, such as in healthcare monitoring, environmental monitoring, smart censoring, and vital decision-making in different professional careers. However, the challenging features of IoT include their involvement in constrained devices such as RFIDs, sensor devices, and mobile phones, which have limited energy resources, communication bandwidth, and memory storage. With the increase in the application of these IoT devices, they become vulnerable to malicious attacks, and thus, the implementation of efficient yet lightweight security protocols is urgently needed [1].Lightweight cryptography involves simplified encryption protocols and schemes with low computational complexity that can be processed on such constrained devices to provide adequate security, considering the limited energy, bandwidth,The associate editor coordinating the review of this manuscript and approving it for publication was Mouloud Denai .

show abstract

“…These constant thermal features will be utilized to match the thermal signature to a specific individual. A technique analogous to fingerprint recognition [ 15 ] is adopted for identifying facial identities.…”

Section: Introductionmentioning

confidence: 99%

Thermal face recognition under different conditions

Lin

Chen

2021

BMC Bioinformatics

View full text Add to dashboard Cite

Background A thermal face recognition under different conditions is proposed in this article. The novelty of the proposed method is applying temperature information in the recognition of thermal face. The physiological information is obtained from the face using a thermal camera, and a machine learning classifier is utilized for thermal face recognition. The steps of preprocessing, feature extraction and classification are incorporated in training phase. First of all, by using Bayesian framework, the human face can be extracted from thermal face image. Several thermal points are selected as a feature vector. These points are utilized to train Random Forest (RF). Random Forest is a supervised learning algorithm. It is an ensemble of decision trees. Namely, RF merges multiple decision trees together to obtain a more accurate classification. Feature vectors from the testing image are fed into the classifier for face recognition. Results Experiments were conducted under different conditions, including normal, adding noise, wearing glasses, face mask, and glasses with mask. To compare the performance with the convolutional neural network-based technique, experimental results of the proposed method demonstrate its robustness against different challenges. Conclusions Comparisons with other techniques demonstrate that the proposed method is robust under less feature points, which is around one twenty-eighth to one sixtieth of those by other classic methods.

show abstract

Deep user identification model with multiple biometric data

Cited by 12 publications

References 12 publications

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

Empirical Studies of TESLA Protocol: Properties, Implementations, and Replacement of Public Cryptography Using Biometric Authentication

Thermal face recognition under different conditions

Contact Info

Product

Resources

About