A survey of emerging biometric modalities

Chauhan, Sushil; Arora, Ajat Shatru; Kaul, Amit

doi:10.1016/j.procs.2010.11.027

Cited by 40 publications

(11 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There have been attempts to organize the existing research in eye tracking for security applications. Most of those focused on the use of physiological biometrics (e.g., iris [24,111,117], retina [24,111,117], periocular [117]) for building and evaluating authentication schemes, without considering the use of gaze-based features. Many works apply gaze-based features to build an authentication scheme, aiming to overcome the limitations that are introduced by the use of physiological biometrics, such as inability to revoke passwords once compromised, or unintentional authentications [146].…”

Section: Related Workmentioning

confidence: 99%

The Role of Eye Gaze in Security and Privacy Applications: Survey and Future HCI Research Directions

Katsini¹,

Abdrabou

Raptis³

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

109

View full text Add to dashboard Cite

For the past 20 years, researchers have investigated the use of eye tracking in security applications. We present a holistic view on gaze-based security applications. In particular, we canvassed the literature and classify the utility of gaze in security applications into a) authentication, b) privacy protection, and c) gaze monitoring during security critical tasks. This allows us to chart several research directions, most importantly 1) conducting field studies of implicit and explicit gaze-based authentication due to recent advances in eye tracking, 2) research on gaze-based privacy protection and gaze monitoring in security critical tasks which are under-investigated yet very promising areas, and 3) understanding the privacy implications of pervasive eye tracking. We discuss the most promising opportunities and most pressing challenges of eye tracking for security that will shape research in gaze-based security applications for the next decade.

show abstract

Section: Related Workmentioning

confidence: 99%

The Role of Eye Gaze in Security and Privacy Applications: Survey and Future HCI Research Directions

Katsini¹,

Abdrabou

Raptis³

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

109

View full text Add to dashboard Cite

show abstract

“…Previous literature reviews of some of the methods that have been applied to ECG based identification can be found in the reports of Nasri et al [28], Sufi et al [29], Chauhan et al [30], and Israel and Irvine [31].…”

mentioning

confidence: 99%

ECG Biometric Recognition: A Comparative Analysis

Odinaka

Lai

Kaplan

et al. 2012

IEEE Trans.Inform.Forensic Secur.

307

196

View full text Add to dashboard Cite

The electrocardiogram (ECG) is an emerging biometric modality that has seen about thirteen years of development, in peer-reviewed literature, and as such deserves a systematic review and discussion of the associated methods and findings. In this paper, we review most of the techniques that have been applied to the use of the electrocardiogram for biometric recognition. In particular, we categorize the methodologies based on the features and the classification schemes. Finally, a comparative analysis of the authentication performance of a few of the ECG biometric systems is presented, using our in-house database. The comparative study includes the cases where training and testing data come from the same and different sessions (days). The authentication results show that most of the algorithms that have been proposed for ECG based biometrics perform well, when the training and testing data come from the same session. However, when training and testing data come from different sessions, a performance degradation occurs. Multiple training sessions were incorporated to diminish the loss in performance. That notwithstanding, only a few of the proposed ECG recognition algorithms appear to be able to support performance improvement due to multiple training sessions. Only three of these algorithms produced This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. 2 equal error rates (EERs) in the single digits, including an EER of 5.5% using a method proposed by us.

show abstract

“…Based on ECG, it is possible to analyze abnormal activities of the heart, such as Bundle Branch Block (BBB), Premature Ventricular Contraction (PVC), and Premature Atrial Contraction (PAC) [12][13][14]. Bundle branch block means the block of the electrical transmission in the right bundle branch or left bundle branch of the heart.…”

Section: Relations Between Ecg Patterns and Cardiovascular Disordersmentioning

confidence: 99%

A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions

2020

View full text Add to dashboard Cite

Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors of personal cardiovascular disorders in real-time. The objective of this paper is to provide the model for the personalized heart condition classification in combination with the fast and effective preprocessing technique and deep neural network in order to process the real-time accumulated biosensor input data. The model can be useful to learn input data and develop an approximation function, and it can help users recognize risk situations. For the analysis of the pulse frequency, a fast Fourier transform is applied in preprocessing work. With the use of the frequency-by-frequency ratio data of the extracted power spectrum, data reduction is performed. To analyze the meanings of preprocessed data, a neural network algorithm is applied. In particular, a deep neural network is used to analyze and evaluate linear data. A deep neural network can make multiple layers and can establish an operation model of nodes with the use of gradient descent. The completed model was trained by classifying the ECG signals collected in advance into normal, control, and noise groups. Thereafter, the ECG signal input in real time through the trained deep neural network system was classified into normal, control, and noise. To evaluate the performance of the proposed model, this study utilized a ratio of data operation cost reduction and F-measure. As a result, with the use of fast Fourier transform and cumulative frequency percentage, the size of ECG reduced to 1:32. According to the analysis on the F-measure of the deep neural network, the model had 83.83% accuracy. Given the results, the modified deep neural network technique can reduce the size of big data in terms of computing work, and it is an effective system to reduce operation time.

show abstract

A survey of emerging biometric modalities

Cited by 40 publications

References 29 publications

The Role of Eye Gaze in Security and Privacy Applications: Survey and Future HCI Research Directions

The Role of Eye Gaze in Security and Privacy Applications: Survey and Future HCI Research Directions

ECG Biometric Recognition: A Comparative Analysis

A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions

Contact Info

Product

Resources

About