The influence of data size on a high-performance computing memetic algorithm in fingerprint dataset

Assiroj, Priati; Warnars, Harco Leslie Hendric Spits; Abdurachman, Edi; Kistijantoro, Achmad Imam; Doucet, Antoine

doi:10.11591/eei.v10i4.2760

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…From the data that appeared in Table 3, it means that fingerprint data processing result test from 15 specimens combined with four testing methods, resulting in a processing time which then processed and sorted [15]. Then see which time is the fastest in data processing between using a single thread loop and multiple thread loops.…”

Section: Figure 8 Sample Groups Of Fingerprint Images Randomlymentioning

confidence: 99%

Reliability Comparison of High Performance Computing between Single Thread Loop and Multiple Thread Loop using Java-Based Programming at Fingerprint Data Processing

Ginanjar

Kusmaya²

2022

JSG

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High Performance Computing is one of the mechanisms in the programming family with a focus on increasing high performance in any programming environment, especially programming languages that use virtual machine environments such as C/C++, Java and Python. several sub-clusters of information technology such as Big Data, Data Warehouse, Business Intelligence and Artificial Intelligence, the use of HPC is widely applied in sophisticated computer machines that have enterprise data processing capabilities. This research was conducted to compare the ability of HPC with algorithms or java programming techniques in data processing that uses a lot of threads when implemented in ordinary computers used in everyday life, the term java programming technique that uses one thread is called Single Thread Loop then when using multiple threads is called Multiple Thread Loop. Due this comparison between sequential and parallel process so this research try to compare between Single Thread Loop and Multiple Thread Loop in Java Programming. The minimum requirement operating system is to use the MS Windows 7 or 10 and a Unix-based OS using an Intel i5 or i7 processor and use a minimum of 16 GB of RAM.

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Section: Figure 8 Sample Groups Of Fingerprint Images Randomlymentioning

confidence: 99%

Reliability Comparison of High Performance Computing between Single Thread Loop and Multiple Thread Loop using Java-Based Programming at Fingerprint Data Processing

Ginanjar

Kusmaya²

2022

JSG

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“…Furthermore, ECG signals are unique and can only be captured by direct physical contact, impenetrable from the outside and includes a liveness indication at the detection location [13]. As a result, in the future of biometric recognition, ECG-based biometrics have the potential to partially or entirely replace other existing biometrics such as vein [14][15][16], gait [17], face [18][19][20][21][22], fingerprint [23,24], and iris [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

ECG in Real World Scenario: Time Variability in Biometric Using Wearable Smart Textile Shirts

Muhammad Muizz Mohd Nawawi,

Khairul Azami Sidek,

Amelia Wong Azman

2024

ARASET

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Biomedical signals, such as an electrocardiogram (ECG), have been included in wearable platforms for biometric reasons due to the rapid expansion of apps and technology capable of gathering this physiological data. Most studies using ECG biometrics are conducted in clinical settings, which is impractical for wearable ECG-based biometric applications. Therefore, this study aims to determine the reliability of ECG signals obtained from the commercially available Hexoskin Proshirt and HeartIn Fit shirt, which may be worn for biometric verification in real-world scenarios. ECG data from 22 participants were collected over a span period of more than 30 days. The raw ECG signal is first pre-processed in the time domain using noise-removal Butterworth filters, and then a successful QRS segmented feature extraction method is used. Not to mention, 300 datasets were used to test the recommended recognition method using a Quadratic Support Vector Machine (QSVM). In comparison, around 854 datasets were prepared for training and validation of the classifier. The findings showed that the proposed method provided a considerable accuracy above 99.63 % with a FAR of 0.14 %, an FRR of 2.86 %, and a TPR of 97.14 %. Thus, the study supports using ECG biometrics for verification in real-world settings by employing a smart textile shirt with varying temporal variability.

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“…they differ between individuals and can only be captured through direct physical contact that is impenetrable from the outside and also carries a liveness indication at the point of detection [7]. Therefore, ECG-based biometric has a vast potential to replace other conventional biometrics completely, such as vein [8], [9] gait [10], face [11], [12], fingerprint [13], [14], and iris [15], [16].…”

Section: Introductionmentioning

confidence: 99%

ECG biometric in real-life settings: analysing different physiological conditions with wearable smart textiles shirts

2023

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The adoption of biomedical signals such as the electrocardiogram (ECG) for biometric is rising in tandem with the increased attention to wearable devices. However, despite its potential benefits, ECG is rarely implemented as a biometric mechanism in real-life wearable applications. Therefore, this research aims to analyse the ECG signals extracted from wearable Hexoskin Proshirt for biometric authentication in different physiological conditions. A total of 11 subjects participated in this study, where the ECG signals were recorded while standing, sitting, walking, and uncontrolled activity. The raw ECG signal is first pre-processed using noise-removal butterworth filters in the time domain, followed by an efficient QRS segmented feature extraction approach. Finally, around 854 datasets were generated for training and validation, while the remaining 300 were used to test the proposed recognition method with a quadratic support vector machine (QSVM). The results show that the proposed method achieved a reliable accuracy above 98% with false acceptance rate (FAR) of 0.93%, false rejection rate (FRR) of 3.64%, and true positive rate (TPR) above 96% on the in-house datasets. This researchs findings confirm the possibility of using ECG biometrics for authentication purposes in various real-life settings with varying physiological parameters using a smart textile shirt.

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The influence of data size on a high-performance computing memetic algorithm in fingerprint dataset

Cited by 4 publications

References 18 publications

Reliability Comparison of High Performance Computing between Single Thread Loop and Multiple Thread Loop using Java-Based Programming at Fingerprint Data Processing

Reliability Comparison of High Performance Computing between Single Thread Loop and Multiple Thread Loop using Java-Based Programming at Fingerprint Data Processing

ECG in Real World Scenario: Time Variability in Biometric Using Wearable Smart Textile Shirts

ECG biometric in real-life settings: analysing different physiological conditions with wearable smart textiles shirts

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