scholarly journals Biometric Recognition: How Do I Know Who You Are?

2005 ◽  
pp. 19-26 ◽  
Author(s):  
Anil K. Jain
Keyword(s):  
Biometric Recognition

Facets and Promises of Gait Biometric Recognition

2018 ◽  
pp. 233-253 ◽  
Author(s):  
James Eric Mason ◽  
Issa Traore ◽  
Isaac Woungang
Keyword(s):  
Biometric Recognition

scholarly journals BioECG: Improving ECG Biometrics with Deep Learning and Enhanced Datasets

2021 ◽  
Vol 11 (13) ◽  
pp. 5880
Author(s):  
Paloma Tirado-Martin ◽  
Raul Sanchez-Reillo
Keyword(s):  
Deep Learning ◽  
Temporal Variations ◽  
Physical Activities ◽  
Learning Tools ◽  
Biometric Recognition ◽  
Learning Network ◽  
Deep Learning Network ◽  
Electrocardiogram Ecg

Nowadays, Deep Learning tools have been widely applied in biometrics. Electrocardiogram (ECG) biometrics is not the exception. However, the algorithm performances rely heavily on a representative dataset for training. ECGs suffer constant temporal variations, and it is even more relevant to collect databases that can represent these conditions. Nonetheless, the restriction in database publications obstructs further research on this topic. This work was developed with the help of a database that represents potential scenarios in biometric recognition as data was acquired in different days, physical activities and positions. The classification was implemented with a Deep Learning network, BioECG, avoiding complex and time-consuming signal transformations. An exhaustive tuning was completed including variations in enrollment length, improving ECG verification for more complex and realistic biometric conditions. Finally, this work studied one-day and two-days enrollments and their effects. Two-days enrollments resulted in huge general improvements even when verification was accomplished with more unstable signals. EER was improved in 63% when including a change of position, up to almost 99% when visits were in a different day and up to 91% if the user experienced a heartbeat increase after exercise.

On the channel density of EEG signals for reliable biometric recognition

2021 ◽  
Author(s):  
Min Wang ◽  
Kathryn Kasmarik ◽  
Anastasios Bezerianos ◽  
Kay Chen Tan ◽  
Hussein Abbass
Keyword(s):  
Eeg Signals ◽  
Biometric Recognition ◽  
Channel Density

scholarly journals Review on Data Acquisition of Electrocardiogram Biometric Recognition in Wearable Smart Textile Shirts

2021 ◽  
Vol 1900 (1) ◽  
pp. 012019
Author(s):  
Muhammad Muizz Mohd Nawawi ◽  
Khairul Azami Sidek ◽  
Alaa K Y Dafhalla ◽  
Amelia Wong Azman
Keyword(s):  
Data Acquisition ◽  
Biometric Recognition ◽  
Smart Textile

Multimodal score level fusion for recognition using face and palmprint

2020 ◽  
pp. 002072092092966
Author(s):  
Milind E Rane ◽  
Umesh S Bhadade
Keyword(s):  
Recognition System ◽  
Acceptance Rate ◽  
Face Database ◽  
Biometric Recognition ◽  
False Acceptance Rate ◽  
Score Level Fusion ◽  
Score Fusion ◽  
Matching Score ◽  
Level Fusion ◽  
Fusion Approach

The paper proposes a t-norm-based matching score fusion approach for a multimodal heterogenous biometric recognition system. Two trait-based multimodal recognition system is developed by using biometrics traits like palmprint and face. First, palmprint and face are pre-processed, extracted features and calculated matching score of each trait using correlation coefficient and combine matching scores using t-norm based score level fusion. Face database like Face 94, Face 95, Face 96, FERET, FRGC and palmprint database like IITD are operated for training and testing of algorithm. The results of experimentation show that the proposed algorithm provides the Genuine Acceptance Rate (GAR) of 99.7% at False Acceptance Rate (FAR) of 0.1% and GAR of 99.2% at FAR of 0.01% significantly improves the accuracy of a biometric recognition system. The proposed algorithm provides the 0.53% more accuracy at FAR of 0.1% and 2.77% more accuracy at FAR of 0.01%, when compared to existing works.

A Review on Biometric Recognition

2014 ◽  
Vol 6 (4) ◽  
pp. 69-76 ◽  
Author(s):  
Gagandeep Kaur ◽  
Gurpreet Singh ◽  
Vineet Kumar
Keyword(s):  
Biometric Recognition
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