Biometric identification using driving behavioral signals

Igarashi, Ken; Miyajima, Chiyomi; Itou, Katunobu; Takeda, Kazuya; Itakura, Fumitada; Abut, H.

doi:10.1109/icme.2004.1394126

Cited by 45 publications

(28 citation statements)

References 6 publications

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“…Table 1 summarizes the data specifications. 5,24,25 Initially, we explored methods based on fast Fourier transform, interdriver, and intradriver distributions; we also explored multidimensional, multichannel extensions of the linear predictive theory. We had limited success in identifying a driver.…”

Section: Driving Behaviormentioning

confidence: 99%

“…We had limited success in identifying a driver. 24,26 Later, to represent each driver's characteristics, we employed Gaussian mixture modeling, a technique regularly and successfully employed in speaker modeling. We used smoothed and subsampled driving signals (acceleration and brake pedal pressures) and their first derivatives as features for statistical modeling.…”

Section: Driving Behaviormentioning

confidence: 99%

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Multimodal Person Recognition for Human-Vehicle Interaction

Erzin¹,

Yemez²,

Tekalp³

et al. 2006

IEEE Multimedia

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Next-generation vehicles will undoubtedly feature biometric person recognition as part of an effort to improve the driving experience. Today's technology prevents such systems from operating satisfactorily under adverse conditions. A proposed framework for achieving person recognition successfully combines different biometric modalities, borne out in two case studies. O ver the past 30 years, the field of biometric person recognition-recognizing individuals according to their physical and behavioral characteristics-has undergone significant progress. Next-generation human−vehicle interfaces will likely incorporate biometric person recognition, using speech, video, images, and analog driver behavior signals to provide more efficient and safer vehicle operation, as well as pervasive and secure in-vehicle communication. Yet, technical and deployment limits hamper these systems' ability to perform satisfactorily in real-world settings under adverse conditions. For instance, environmental noise and changes in acoustic and microphone conditions can significantly degrade speaker recognition performance. Similarly, factors such as illumination and background variation, camera resolution and angle, and facial expressions contribute to performance loss in visually identifying a person. Biometric person recognition in vehicles is especially likely to challenge researchers because of difficulties posed by the vehicle's interior compartment as well as by economics.In this article, we present an overview of multimodal in-vehicle person recognition technologies. We demonstrate, through a discussion of our proposed framework, that the levels of accuracy required for person recognition can be achieved by fusing multiple modalities. We discuss techniques and prominent research efforts, and we present the results of two case studies we conducted. The sidebar, "Solutions for In-Vehicle Person Recognition," discusses related work. Why in-vehicle person recognition?To improve the driving experience, making it better and safer, manufacturers are making vehicles smarter. As vehicles become smarter, information processing from vehicle sensors will become more complex, as will vehicle personalization, which involves adapting the driver and passenger compartment for safer driving and travel.Personalization features will, for example, increase vehicle safety by determining whether the person behind the wheel is an authorized driver (say, the vehicle's legal owner). If so, the driver will be able to operate the vehicle. If the individual isn't authorized, the vehicle will prevent operation and could communicate with authorities to report the incident and initiate an enforcement procedure, such as preventing the driver from starting the ignition.Personalization will promote safe driving by monitoring a driver's behavior. Once the human−vehicle interface has identified a driver and determined road and traffic conditions, the vehicle can monitor the driver's behavioral signals from braking, accelerating, or swerving. With these signals, the hum...

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Section: Driving Behaviormentioning

confidence: 99%

Section: Driving Behaviormentioning

confidence: 99%

Multimodal Person Recognition for Human-Vehicle Interaction

Erzin¹,

Yemez²,

Tekalp³

et al. 2006

IEEE Multimedia

Self Cite

View full text Add to dashboard Cite

show abstract

“…For in-vehicle authentication, the integral system is expected to monitor the driver-specific features [129,130], which could be analyzed from two perspectives: (i) vehicle-specific behavior: steering angle sensor, speed sensor, brake pressure sensor, etc. [131,132]; and (ii) human factors: music played, calls made, presence of people in the car, etc.…”

Section: Behavior Detectionmentioning

confidence: 99%

Multi-Factor Authentication: A Survey

et al. 2018

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Today, digitalization decisively penetrates all the sides of the modern society. One of the key enablers to maintain this process secure is authentication. It covers many different areas of a hyper-connected world, including online payments, communications, access right management, etc. This work sheds light on the evolution of authentication systems towards Multi-Factor Authentication (MFA) starting from Single-Factor Authentication (SFA) and through Two-Factor Authentication (2FA). Particularly, MFA is expected to be utilized for human-to-everything interactions by enabling fast, user-friendly, and reliable authentication when accessing a service. This paper surveys the already available and emerging sensors (factor providers) that allow for authenticating a user with the system directly or by involving the cloud. The corresponding challenges from the user as well as the service provider perspective are also reviewed. The MFA system based on reversed Lagrange polynomial within Shamir's Secret Sharing (SSS) scheme is further proposed to enable more flexible authentication. This solution covers the cases of authenticating the user even if some of the factors are mismatched or absent. Our framework allows for qualifying the missing factors by authenticating the user without disclosing sensitive biometric data to the verification entity. Finally, a vision of the future trends in MFA is discussed.

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“…(2) Identifying drivers based on low-level signals using neural network with feature extraction and model construction: the proposed algorithms focus on using low-level signals from inertial sensors found in offthe-shelf devices (e.g., smartphones) unlike previous algorithms which require specialized sensors and/or driving simulators [7,8,[11][12][13][14]. The challenge is that, before being effectively deployed in real-world scenarios, driver identification algorithms will have to learn many distinct behaviors.…”

Section: Introductionmentioning

confidence: 99%

“…(1) Using unsupervised anomaly detection to minimize the necessity of assessing every input signal: driver identification usually involves recognition of numerous different patterns and previously proposed algorithms often find it necessary to recognize and assess every instant of input signals [7,[11][12][13]. However, for realtime applications, recognizing patterns by assessing every instant of input signal may not be practical as it can incur delays and also consume computational resources.…”

Section: Introductionmentioning

confidence: 99%

Combining Unsupervised Anomaly Detection and Neural Networks for Driver Identification

Tanprasert

Saiprasert

Thajchayapong

2017

Journal of Advanced Transportation

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This paper proposes an algorithm for real-time driver identification using the combination of unsupervised anomaly detection and neural networks. The proposed algorithm uses nonphysiological signals as input, namely, driving behavior signals from inertial sensors (e.g., accelerometers) and geolocation signals from GPS sensors. First anomaly detection is performed to assess if the current driver is whom he/she claims to be. If an anomaly is detected, the algorithm proceeds to find relevant features in the input signals and use neural networks to identify drivers. To assess the proposed algorithm, real-world data are collected from ten drivers who drive different vehicles on several routes in real-world traffic conditions. Driver identification is performed on each of the sevensecond-long driving behavior signals and geolocation signals in a streaming manner. It is shown that the proposed algorithm can achieve relatively high accuracy and identify drivers within 13 seconds. The proposed algorithm also outperforms the previously proposed driver identification algorithms. Furthermore, to demonstrate how the proposed algorithm can be deployed in real-world applications, results from real-world data associated with each operation of the proposed algorithm are shown step-by-step.

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Biometric identification using driving behavioral signals

Cited by 45 publications

References 6 publications

Multimodal Person Recognition for Human-Vehicle Interaction

Multimodal Person Recognition for Human-Vehicle Interaction

Multi-Factor Authentication: A Survey

Combining Unsupervised Anomaly Detection and Neural Networks for Driver Identification

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