Your face your heart: Secure mobile face authentication with photoplethysmograms

Chen, Yimin; Sun, Jing; Jin, Xiaocong; Li, Tao; Zhang, Rui; Zhang, Yanchao

doi:10.1109/infocom.2017.8057220

Cited by 26 publications

(10 citation statements)

References 21 publications

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“…(3) The paper had to propose an authentication scheme. Specifically, the paper had to use machine learning to label [12], [33], [41], [42] IMWUT/UbiComp [11], [20], [25], [35], [48] INFOCOM [8], [36], [45], [51], [53] MobiCom [15], [32] MobiSys [7], [31] NDSS [5], [17], [49], [50] Pattern Recognition [2], [9], [18], [24], [38], [40], [54]…”

Section: Review Of Recent Authentication Systemsmentioning

confidence: 99%

Robust Performance Metrics for Authentication Systems

Sugrim

Liu

McLean³

et al. 2019

Proceedings 2019 Network and Distributed System Security Symposium

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Research has produced many types of authentication systems that use machine learning. However, there is no consistent approach for reporting performance metrics and the reported metrics are inadequate. In this work, we show that several of the common metrics used for reporting performance, such as maximum accuracy (ACC), equal error rate (EER) and area under the ROC curve (AUROC), are inherently flawed. These common metrics hide the details of the inherent tradeoffs a system must make when implemented. Our findings show that current metrics give no insight into how system performance degrades outside the ideal conditions in which they were designed. We argue that adequate performance reporting must be provided to enable meaningful evaluation and that current, commonly used approaches fail in this regard. We present the unnormalized frequency count of scores (FCS) to demonstrate the mathematical underpinnings that lead to these failures and show how they can be avoided. The FCS can be used to augment the performance reporting to enable comparison across systems in a visual way. When reported with the Receiver Operating Characteristics curve (ROC), these two metrics provide a solution to the limitations of currently reported metrics. Finally, we show how to use the FCS and ROC metrics to evaluate and compare different authentication systems.

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Section: Review Of Recent Authentication Systemsmentioning

confidence: 99%

Robust Performance Metrics for Authentication Systems

Sugrim

Liu

McLean³

et al. 2019

Proceedings 2019 Network and Distributed System Security Symposium

View full text Add to dashboard Cite

show abstract

“…• Once an accepting sample via the feature vector API has been found, it may be possible to obtain an input that results in this sample (after feature extraction), as demonstrated by Garcia et al with the training of an autoencoder for both feature extraction and the regeneration of the input image [23]. • In this work, we have focused on authentication as a binary classification problem, largely because of its widespread use in biometric authentication [8], [9], [10], [11], [12], [13], [14], [15], [16], [26]. However, authentication has also been framed as a one-class classification problem [56], [26] or as multi-class classification [26], e.g., in a discrimination model, as noted earlier.…”

Section: Discussionmentioning

confidence: 99%

“…The target user's data for training is obtained during the registration or enrollment phase. For the negative class, the usual process is to use the data of a subset of other users enrolled in the system [27], [8], [7], [9], [10], [11], [12], [13], [14], [15], [16]. Following best machine learning practice, the data (from both classes) is split into a training and test set.…”

Section: A Biometric Authentication Systemsmentioning

confidence: 99%

“…These systems are mostly based on machine learning: a binary classifier is trained on the target user's data (positive class) and a subset of data from other users (negative class). This process is used to validate the performance of the machine learning classifier and hence the biometric system [8], [7], [9], [10], [11], [12], [13], [14], [15], [16]. The resulting proportion of negative samples (other users' data) successfully gaining access (when they should have been rejected) produces the false positive rate (FPR, also referred as False Acceptance Rate).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On the Resilience of Biometric Authentication Systems against Random Inputs

Zhao¹,

Asghar²,

Kâafar³

2020

Proceedings 2020 Network and Distributed System Security Symposium

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We assess the security of machine learning based biometric authentication systems against an attacker who submits uniform random inputs, either as feature vectors or raw inputs, in order to find an accepting sample of a target user. The average false positive rate (FPR) of the system, i.e., the rate at which an impostor is incorrectly accepted as the legitimate user, may be interpreted as a measure of the success probability of such an attack. However, we show that the success rate is often higher than the FPR. In particular, for one reconstructed biometric system with an average FPR of 0.03, the success rate was as high as 0.78. This has implications for the security of the system, as an attacker with only the knowledge of the length of the feature space can impersonate the user with less than 2 attempts on average. We provide detailed analysis of why the attack is successful, and validate our results using four different biometric modalities and four different machine learning classifiers. Finally, we propose mitigation techniques that render such attacks ineffective, with little to no effect on the accuracy of the system.

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“…For physical characteristics, Chen et al [4] proposed an authentication method using video images of the user's face and fingertips captured from the front and rear cameras of a mobile device. Siddharth et al [5] proposed an authentication system based on the palm print and palm vein.…”

Section: User Authentication Methodsmentioning

confidence: 99%

User Identification Method based on Head Shape Using Pressure Sensors Embedded in a Helmet

Fujii

Murao

2021

Journal of Information Processing

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Various types of helmets exist, including industrial protective helmets, motorcycle helmets, sports helmets, and military/police helmets. By identifying individuals wearing a helmet, their name, affiliation, and qualification can be presented on a display mounted on the helmet, and sensor data collected through the helmet, such as acceleration, video, and eye-tracking data, can be labeled with the user's ID. In this paper, we propose a user identification method based on head shape using a helmet equipped with 32 pressure sensors. Our method has two functions: user identification and authentication. User identification is based on the assumption that a single helmet is shared by multiple individuals. The goal of this method is to identify which of the registered people is the person wearing the helmet. User authentication determines whether the individual wearing the helmet is the individual with the ID when the ID is provided to the system. In the evaluation, we obtained sensor values for 2 seconds 20 times from nine subjects as head shape data. The accuracy was evaluated using 5-fold cross-validation, and we achieved 100% accuracy with five sensors and 92% with two sensors for user identification and an average equal error rate of 0.076 with 32 sensors for user authentication.

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Your face your heart: Secure mobile face authentication with photoplethysmograms

Cited by 26 publications

References 21 publications

Robust Performance Metrics for Authentication Systems

Robust Performance Metrics for Authentication Systems

On the Resilience of Biometric Authentication Systems against Random Inputs

User Identification Method based on Head Shape Using Pressure Sensors Embedded in a Helmet

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