Performance of gait authentication using an acceleration sensor

Enomoto, Yusuke; Dai, Honghua; Oguchi, Kimio

doi:10.1109/tsp.2011.6043780

Cited by 20 publications

(14 citation statements)

References 5 publications

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“…They are then stored as referred templates corresponding to individual. Various distance metrics such as Dynamic Time Warping (DTW) [9,14,19], Euclidean distance [8,9], autocorrelation [13], nearest neighbors [11] are used for calculating the similarity score between a given pattern and referred templates.…”

Section: State Of the Artmentioning

confidence: 99%

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A Lightweight Gait Authentication on Mobile Phone Regardless of Installation Error

Hoang

Choi

et al. 2013

Security and Privacy Protection in Information Processing Systems

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Abstract. In this paper, we propose a novel gait authentication mechanism by mining sensor resources on mobile phone. Unlike previous works, both built-in accelerometer and magnetometer are used to handle mobile installation issues, including but not limited to disorientation, and misplacement errors. The authentication performance is improved by executing deep examination at pre-processing steps. A novel and effective segmentation algorithm is also provided to segment signal into separate gait cycles with perfect accuracy. Subsequently, features are then extracted on both time and frequency domains. We aim to construct a lightweight but high reliable model; hence feature subsets selection algorithms are applied to optimize the dimension of the feature vectors as well as the processing time of classification tasks. Afterward, the optimal feature vector is classified using SVM with RBF kernel. Since there is no public dataset in this field to evaluate fairly the effectiveness of our mechanism, a realistic dataset containing the influence of mobile installation errors and footgear is also constructed with the participation of 38 volunteers (28 males, 10 females). We achieved the accuracy approximately 94.93% under identification mode, the FMR, FNMR of 0%, 3.89% and processing time of less than 4 seconds under authentication mode.

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Section: State Of the Artmentioning

confidence: 99%

“…Since 2009, this study has been initiated on mobile and achieved encouraging results [8,19]. However, they were still in early stages and methods were tried-out on ideal conditions in which mobiles were always installed at an exact position and orientation by tightening directly to equipment such as suite, footgear, or human body.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Gait Authentication on Mobile Phone Regardless of Installation Error

Hoang

Choi

et al. 2013

Security and Privacy Protection in Information Processing Systems

View full text Add to dashboard Cite

show abstract

“…They are then stored as referred templates that correspond to the individual. Various distance metrics, such as Dynamic Time Warping (DTW) [9,19,14], Euclidean distance [8,9], auto-correlation [13], and nearest neighbors [11] are used for calculating the similarity score between a given pattern and the referred templates. The ML method is the most popular approach that is used in pattern recognition areas.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Cross-Device Gait Recognition Using a Mobile Accelerometer

Hoang¹,

Nguyen²,

Luong³

et al. 2013

Journal of Information Processing Systems

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Abstract-Mobile authentication/identification has grown into a priority issue nowadays because of its existing outdated mechanisms, such as PINs or passwords. In this paper, we introduce gait recognition by using a mobile accelerometer as not only effective but also as an implicit identification model. Unlike previous works, the gait recognition only performs well with a particular mobile specification (e.g., a fixed sampling rate). Our work focuses on constructing a unique adaptive mechanism that could be independently deployed with the specification of mobile devices. To do this, the impact of the sampling rate on the preprocessing steps, such as noise elimination, data segmentation, and feature extraction, is examined in depth. Moreover, the degrees of agreement between the gait features that were extracted from two different mobiles, including both the Average Error Rate (AER) and Intra-class Correlation Coefficients (ICC), are assessed to evaluate the possibility of constructing a device-independent mechanism. We achieved the classification accuracy approximately 91.33 ± 0.67 % for both devices, which showed that it is feasible and reliable to construct adaptive cross-device gait recognition on a mobile phone.

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“…As wearable positions, waist [48], [49], [50], ankle [51], waist and ankle [52], arm [53], knee [54], shoe [55], [56], [57] and backpack [58] have been proposed (for another examples, see [59], [60]). Moreover, this is recognized as a promising approach from the view point of continuous access control for smart phones as well as buildings [33].…”

Section: B Conventional Contributions Of Gait Recognitionmentioning

confidence: 99%

Biometric gait verification by horizontal swings in frontal manner towards human-aware environment

Manabe

Saito

Sugawara

2012

2012 IEEE 11th International Conference on Cognitive Informatics and Cognitive Computing

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Over half a century has passed since the first proposal of 'human-computer symbiosis' by Licklider[1]. However people cannot yet receive sufficient and suitable services without their proactive actions because systems cannot recognize dynamic situation in the Real Space. In order to develop a human-aware environment in the name of 'human-computer symbiosis', we target human verification problem and develop gait verification function by movement time series based on body-skeleton information. In this paper, we employ a portable video game device 'Kinect c ', which is used for measuring human body-skeleton information. As the result of gait verification simulation, we show that our developed function works successfully in spite of simple approach and an easy-to-use device.

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Performance of gait authentication using an acceleration sensor

Cited by 20 publications

References 5 publications

A Lightweight Gait Authentication on Mobile Phone Regardless of Installation Error

A Lightweight Gait Authentication on Mobile Phone Regardless of Installation Error

Adaptive Cross-Device Gait Recognition Using a Mobile Accelerometer

Biometric gait verification by horizontal swings in frontal manner towards human-aware environment

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