Biometric personal authentication by one step foot pressure distribution change by load distribution sensor

Takeda, Tomotaka; Taniguchi, Koichi; Asari, K. Vijayan; Kuramoto, Kei; Kobashi, Syoji; Hata, Yutaka

doi:10.1109/fuzzy.2009.5277149

Cited by 18 publications

(19 citation statements)

References 6 publications

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“…Regardless, Jung et al 's results imply that a larger database of subjects may be identifiable even during unnatural or constrained gait. The remaining studies examined fewer than 12 subjects (except for [15]: CR ¼ 86.1%, n ¼ 32) and reported moderate CRs in the range 64-94%.…”

Section: Previous Studiesmentioning

confidence: 99%

“…Only one study examined more than 11 subjects [15] (n ¼ 30), but accuracy was notably lower (86.1%) than a previous study by the same group with fewer subjects-93.1 per cent (n ¼ 10) [13]. To date, high accuracies on samples notably larger than n ¼ 10 have only been achieved using complimentary information like high-resolution skin prints (99%; n ¼ 32) [16] or three-dimensional foot sole shape (98.7%; n ¼ 30) [17], information that cannot be readily obtained during uninterrupted gait because of lengthy scanning durations.…”

Section: Introductionmentioning

confidence: 99%

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Gait recognition: highly unique dynamic plantar pressure patterns among 104 individuals

Pataky

Bosch

et al. 2011

J. R. Soc. Interface.

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Everyone's walking style is unique, and it has been shown that both humans and computers are very good at recognizing known gait patterns. It is therefore unsurprising that dynamic foot pressure patterns, which indirectly reflect the accelerations of all body parts, are also unique, and that previous studies have achieved moderate-to-high classification rates (CRs) using foot pressure variables. However, these studies are limited by small sample sizes (n , 30), moderate CRs (CR ≃ 90%), or both. Here we show, using relatively simple image processing and feature extraction, that dynamic foot pressures can be used to identify n ¼ 104 subjects with a CR of 99.6 per cent. Our key innovation was improved and automated spatial alignment which, by itself, improved CR to over 98 per cent, a finding that pointedly emphasizes inter-subject pressure pattern uniqueness. We also found that automated dimensionality reduction invariably improved CRs. As dynamic pressure data are immediately usable, with little or no pre-processing required, and as they may be collected discreetly during uninterrupted gait using in-floor systems, foot pressure-based identification appears to have wide potential for both the security and health industries.

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Section: Previous Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Gait recognition: highly unique dynamic plantar pressure patterns among 104 individuals

Pataky

Bosch

et al. 2011

J. R. Soc. Interface.

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“…We compared the combined fuzzy degrees Sole ( ) with the fuzzy degree of right sole ( ) and left sole ( ). In our past works, [13] authenticated person by only right sole pressure data. Reference [14] authenticated person by using mean value of right and left sole fuzzy degrees.…”

Section: Frr = Number Of False Rejections Number Of Authorized Attemptsmentioning

confidence: 99%

“…Reference [14] authenticated person by using mean value of right and left sole fuzzy degrees. Thus, [13,14] correspond methods using the fuzzy degree of right sole ( ) and mean operator, respectively. We compared authentication results of these combination operators with results of an authentication method based on Euclidean distance.…”

Section: Frr = Number Of False Rejections Number Of Authorized Attemptsmentioning

confidence: 99%

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On Optimal Operator for Combining Left and Right Sole Pressure Data in Biometrics Security

Takeda

Kuramoto

Kobashi

et al. 2013

Advances in Fuzzy Systems

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This paper describes optimal operator for combining left and right sole pressure data in a personal authentication method by dynamic change of sole pressure distribution while walking. The method employs a pair of right and left sole pressure distribution change data. These data are acquired by a mat-type load distribution sensor. The system extracts features based on shape of sole and weight shift from each sole pressure distribution. We calculate fuzzy degrees of right and left sole pressures for a registered person. Fuzzy if-then rules for each registered person are statistically determined by learning data set. Next, we combine the fuzzy degrees of right and left sole pressure data. In this process, we consider six combination operators. We examine which operator achieves best accuracy for the personal authentication. In the authentication system, we identify the walking persons as a registered person with the highest fuzzy degree. We verify the walking person as the target person when the combined fuzzy degree of the walking person is higher than a threshold. In our experiment, we employed 90 volunteers, and our method obtained higher authentication performance by mean and weighted sum operators.

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User authentication based on foot motion

Gafurov

Bours

Snekkenes

2011

SIViP

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In nearly all current systems, user authentication mechanism is one time and static. Although such type of user authentication is sufficient for many types of applications, in some scenarios, continuous or periodic re-verification of the identity is desirable, especially in high-security application. In this paper, we study user authentication based on 3D foot motion, which can be suitable for periodic identity re-verification purposes. Three-directional (3D) motion of the foot (in terms of acceleration signals) is collected using a wearable accelerometer sensor attached to the ankle of the person. Ankle accelerations from three directions (up-down, forward-backward and sideways) are analyzed for person authentication. Applied recognition method is based on detecting individual cycles in the signal and then finding best matching cycle pair between two acceleration signals. Using experimental data from 30 subjects, obtained EERs (Equal Error Rates) were in the range of 1.6-23.7% depending on motion directions and shoe types. Furthermore, by combining acceleration signals from 2D and 3D and then applying fusing techniques, recognition accuracies could be improved even further. The achieved performance improvements (in terms of EER) were up to 68.8%.

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Biometric personal authentication by one step foot pressure distribution change by load distribution sensor

Cited by 18 publications

References 6 publications

Gait recognition: highly unique dynamic plantar pressure patterns among 104 individuals

Gait recognition: highly unique dynamic plantar pressure patterns among 104 individuals

On Optimal Operator for Combining Left and Right Sole Pressure Data in Biometrics Security

User authentication based on foot motion

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