Detection of Facial Spoofing Attacks in Uncontrolled Environments Using ELBP and Color Models

Valderrama, Wendy; Salazar, Andrea Magadán; Villegas, Osslan Osiris Vergara; Ruiz, José; Elías, Raúl Pinto; Reyes, Gerardo

doi:10.1109/tla.2022.9757369

Cited by 5 publications

(3 citation statements)

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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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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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“…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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“…They can only be collected by direct physical contact that is impenetrable from the outside and contains a liveness indicator at the detection site [13]. As a result, ECG-based biometrics have the potential to completely replace other traditional biometrics, such as vein, gait, face, fingerprint, and iris in the near future of biometric recognition [14][15][16][17][18][19][20][21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Biometric Analysis on Smart Textile Garment in Real Life Scenario

Muhammad Muizz Mohd Nawawi,

Khairul Azami Sidek,

Amelia Wong Azman

2023

ARASET

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The rapid proliferation of wearable applications and technologies capable of acquiring biomedical signals has prompted the incorporation of biomedical signals, such as the electrocardiogram (ECG), for biometric purposes in wearable platforms. Most ECG biometric research utilises medical-grade sensors in clinical settings, which is unrealistic for wearable ECG-based biometric applications in the real world. Therefore, this research aims to examine the ECG biometric on smart textile garments in real life, collected from commercially available wearable Hexoskin Proshirt and HeartIn Fit shirts. ECG data were obtained from 22 participants who took part in this study. The raw ECG signal is initially pre-processed using noise-removal Butterworth filters in the time domain, followed by an effective QRS segmented feature extraction technique. Finally, around 2076 datasets were created for training and validation, while the remaining 501 datasets were employed to test the suggested recognition approach with 29 Machine Learning Classifiers. Subsequently, Quadratic SVM has the highest accuracy at 96.8% for ECG biometrics, followed by Narrow Neural Network with 95.8% and Wide Neural Network with 95.4%. Further improvement to the QSVM parameter improved the accuracy to 97.4% with an error rate of 2.6%, followed by a sensitivity of 97.4% with a precision of 97.7% and a false rejection rate of 2.6%. Thus, the results of this study further validate the feasibility of applying ECG biometrics for recognition in real-life scenarios utilising a smart textile shirt with different configurations and brand is possible.

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Detection of Facial Spoofing Attacks in Uncontrolled Environments Using ELBP and Color Models

Cited by 5 publications

References 0 publications

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

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

Biometric Analysis on Smart Textile Garment in Real Life Scenario

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