A Study on Biometric Authentication based on Arm Sweep Action with Acceleration Sensor

Okumura, Fuminori; Kubota, A.; Hatori, Yoshinori; Matsuo, Kenji; Hashimoto, Masayuki; Koike, Atsushi

doi:10.1109/ispacs.2006.364871

Cited by 54 publications

(35 citation statements)

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“…The basic arm swing authentication is relatively effective as reported in [4]. However the False Rejection Rate deteriorates over time, because the determination algorithm does not reflect the alteration of the arm swing.…”

Section: Proposed Methodsmentioning

confidence: 96%

“…Conversely, the error angle increases when the directions differ. Although arm strengths often cause the amplitude of the acceleration signal to vary with every trial, the error angle is independent of the amplitude; hence the latter need not be taken into account [4].…”

Section: Error Anglementioning

confidence: 99%

“…As one of those research activities, we have proposed a new means of biometric identification, based on the similarity of arm swing motion [4]. Since arm swing motions are captured naturally through everyday behavior, they are suitable as a means of personal identification on mobile terminals, such as cellular phones.…”

Section: Introductionmentioning

confidence: 99%

“…However, it is difficult to acquire arm swing tracks precisely due to the accumulation of drift errors due to the noise of the motion sensor. Therefore, we proposed an identification method using an arm swing motion acquired by an acceleration sensor rather than a track [4]. The method represents the basic element of the method proposed in this paper and is thus described in detail in the following subsections.…”

Section: Introductionmentioning

confidence: 99%

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Arm Swing Identification Method with Template Update for Long Term Stability

Matsuo

Okumura

Hashimoto

et al.

Advances in Biometrics

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Abstract. This paper proposes a novel method for biometric identification, based on arm swing motions with a template update in order to improve long term stability. In our previous work, we studied arm swing identification and proposed a basic method to realize a personal identification function on mobile terminals. The method compares the acceleration signals of arm swing motion as individual characteristics, with the tolerant similarity measurement between two arm swing motions via DP-matching, which enables users to unlock a mobile terminal simply by swinging it. However, the method has a problem with long term stability. In other words, the arm swing motions of identical individuals tend to fluctuate among every trial. Furthermore, the difference between the enrolled and trial motions increases over time. Therefore in this paper, we propose an update approach to the enrollment template for DPmatching to solve this problem. We employ an efficient adaptive update method using a minimum route determination algorithm in DP-matching. Identification experiments involving 12 persons over 6 weeks confirm the proposed method achieves a superior equal error rate of 4.0% than the conventional method, which has an equal error rate of 14.7%.

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Section: Proposed Methodsmentioning

confidence: 96%

Section: Error Anglementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Arm Swing Identification Method with Template Update for Long Term Stability

Matsuo

Okumura

Hashimoto

et al.

Advances in Biometrics

Self Cite

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show abstract

“…Meanwhile, using hand gesture as a biometric for authentication is a promising solution. The sweep or swing of action user while holding the mobile device can be used as biometric for authentication [12,14]. Yet it's not suitable for wearables since the device is not fixed on the wrist.…”

Section: Related Workmentioning

confidence: 99%

User-Authentication on Wearable Devices Based on Punch Gesture Biometrics

Liang

2017

ITM Web Conf.

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Abstract. Due to commoditization and convenience, wearable technology are interwoven with our daily life. However, privacy sensitive data stored on those devices such as personal email, message can be easily stolen. Most devices require a PIN input to unlock. However, this mechanism is vulnerable to shoulder surfing attack. Thus many novel authentication approaches have been proposed to solve this problem. And biometric-based methods have been adopted by many researchers because of the efficiency and excellent performance. In this paper, we propose a new biometric-based authentication system. We focus on how the user performs a straight punch gesture subconsciously. By analysis the acceleration data from the smartwatch when user performing the gesture, we are able to profile the user. And we authenticate the user according to the biometrics of this action. This mechanism is light-weighted and do not require user to remember any secret code. We develop an authentication system on Samsung Gear Fit 2 and conducted a real-world experiment on 20 volunteers. And we collected 13000 gesture samples to evaluate our system. Results show that our system can achieve a classification accuracy of at least 95.45%. In attacking scenario, our system can achieve an equal error rate lower than 4%. The maximum number of samples required by a well-trained classifier is 25.

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