Security Vulnerabilities Against Fingerprint Biometric System

Joshi, Mahesh; Mazumdar, Bodhisatwa; Dey, Somnath

doi:10.48550/arxiv.1805.07116

Cited by 3 publications

(3 citation statements)

References 91 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The physiological biometrics mentioned include fingerprint, face, hand, iris, ear acoustic, vascular patterns, electrocardiogram (ECG) and deoxyribonucleic acid (DNA). However, Joshi et al [27] performed a security analysis on the fingerprint system alone and confirmed that there were 16 possible attack points. Devices such as smartphones are using biometric authentication systems, such as facial authentication (FA) techniques.…”

Section: Related Workmentioning

confidence: 98%

Classifying Participant Standing and Sitting Postures Using Channel State Information

Custance,

Khan,

Parkinson

2023

Electronics

View full text Add to dashboard Cite

Recently, channel state information (CSI) has been identified as beneficial in a wide range of applications, ranging from human activity recognition (HAR) to patient monitoring. However, these focused studies have resulted in data that are limited in scope. In this paper, we investigate the use of CSI data obtained from an ESP32 microcontroller to identify participants from sitting and standing postures in a many-to-one classification. The test is carried out in a controlled isolated environment to establish whether a pre-trained model can distinguish between participants. A total of 15 participants were recruited and asked to sit and stand between the transmitter (Tx) and the receiver (Rx), while their CSI data were recorded. Various pre-processing algorithms and techniques have been incorporated and tested on different classification algorithms, which have gone through parameter selection to enable a consistent testing template. Performance metrics such as the confusion matrix, accuracy, and elapsed time were captured. After extensive evaluation and testing of different classification models, it has been established that the hybrid LSTM-1DCNN model has an average accuracy of 84.29% and 74.13% for sitting and standing postures, respectively, in our dataset. The models were compared with the BedroomPi dataset and it was found that LSTM-1DCNN was the best model in terms of performance. It is also the most efficient model with respect to the time elapsed to sit and stand.

show abstract

Section: Related Workmentioning

confidence: 98%

Classifying Participant Standing and Sitting Postures Using Channel State Information

Custance,

Khan,

Parkinson

2023

Electronics

View full text Add to dashboard Cite

show abstract

“…All these methods have their advantages and disadvantages. The finger scanner enables costeffective, secure and personalized authentication [95,4]. A similar method is the scan of the hand geometry, in which hand vein patterns or geometric features of the hand can be captured [102,103].…”

Section: Introductionmentioning

confidence: 99%

Different stimuli and machine learning methods for person identification

Luce,

Bernike

2024

Preprint

View full text Add to dashboard Cite

This study delves into the identification of individuals through gaze patterns, employing a variety of stimuli and machine learning techniques. The objective is to discern the impact of diverse stimuli on the precision of identifying individuals via their eye movements. By harnessing MATLAB and neural networks, the coordinates for each frame were meticulously extracted, facilitating the classification of individuals with a classification learner. The research encompassed 55 participants. The results show significant differences in accuracy, with stimuli performing better and person identification based on eye movements alone being successful. This approach provides insights for use in biometric authentication and contributes to the further development of gaze-based identification systems.

show abstract

“…All these methods have their advantages and disadvantages. The finger scanner enables costeffective, secure and personalized authentication [97,4]. A similar method is the scan of the hand geometry, in which hand vein patterns or geometric features of the hand can be captured [104,105].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of eye-tracking-based personal identification

Luce,

Bernike

2024

Preprint

View full text Add to dashboard Cite

show abstract

Security Vulnerabilities Against Fingerprint Biometric System

Cited by 3 publications

References 91 publications

Classifying Participant Standing and Sitting Postures Using Channel State Information

Classifying Participant Standing and Sitting Postures Using Channel State Information

Different stimuli and machine learning methods for person identification

Evaluation of eye-tracking-based personal identification

Contact Info

Product

Resources

About