Biometric verification using the motion of fingers: a combination of physical and behavioural biometrics

Nishiuchi, Nobuyuki; Komatsu, Shojiro; Yamanaka, Kazushi

doi:10.1504/ijbm.2010.033387

Cited by 11 publications

(6 citation statements)

References 7 publications

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“…The fingertip location is decided based on the horizontal maximum point of the finger outline by horizontal scanning. For the feature extraction processing, the finger outline pixels are pursued in an anticlockwise direction from the starting point until returning to that point (Nishiuchi, 2010), and the distance between pixels on the finger outline and the middle of the two dots (between B c and R c ) on the artifact is measured continuously. The starting point for pursuing the finger outline is detected based on the intersection between the finger outline and the extended line connecting points R c and B c on the artifact (Figure 7(b)).…”

Section: Feature Extractionmentioning

confidence: 99%

Cancelable Biometric Identification by Combining Biological Data with Artifacts

Nishiuchi

Soya

2011

2011 International Conference on Biometrics and Kansei Engineering

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Section: Feature Extractionmentioning

confidence: 99%

Cancelable Biometric Identification by Combining Biological Data with Artifacts

Nishiuchi

Soya

2011

2011 International Conference on Biometrics and Kansei Engineering

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“…The finger outline is then obtained by edge extraction using a Laplacian filter (Figure 6(c)). For the feature extraction processing, the finger outline pixels are pursued in an anticlockwise direction from the starting point until returning to that point (Nishiuchi, 2010), and the distance between pixels on the finger outline and the middle of the two dots (between B c and R c ) on the artifact is measured continuously. The starting point for pursuing the finger outline is detected based on the intersection between the finger outline and the extended line connecting points R c and B c on the artifact (Figure 7(b)).…”

Section: Image Processing For Fingersmentioning

confidence: 99%

Recent Application in Biometrics

Yang¹

2011

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and Economics, China. He obtained his B.S. degree from South-Central University for Nationalities, China, and M.S. and Ph.D. degree from Chonbuk National University, Republic of Korea. He made his post-doc research at the Advanced Graduate Education Center of Jeonbuk for Electronics and Information Technology-BK21 (AGECJEIT-BK21), Republic of Korea. He has published over 40 papers in related international journals and conferences. He has served as one of the editors and reviewers of international journals such as IEEE Transactions on Information Forensics & Security, Neurocomputing, Information Technology Journal and as reviewer of many conferences such as VISAPP'06, ICNC'06-FSKD'06, IPCV'08, IMPRESS'09. He is the registration chair of ICMeCG'09, and the publicity chair of ICMeCG'10, ICMeCG'11. He holds a Chinese patent of biometrics too. His research interests include biometrics, image processing, pattern recognition and neural networks.

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“…Although there is much research on behavioral authentication, a few researches explore the authentication behavior types. Nishiuchi et al [24], [25] conducted research on using the bending motion of fingers for behavioral authentication. Imura and Hosobe [19] utilized both the movement of fingertips and finger joints as input for authentication.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Hand and Finger-Issued Behaviors Toward Natural Authentication

et al. 2020

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As wearables, such as smartwatches and data gloves, become ubiquitous in our daily lives, it is possible to capture users' behaviors as they go, and then to authenticate them using biometrics. A variety of behaviors have been exploited to authenticate users in a plethora of scenarios. However, there is still a lack of systematic investigations on what types of behaviors present better performance for authentication, and therefore, limiting our understanding of the custom design of behavioral ''passwords'' to fit people's needs. This paper is designed to help answer this question. First, we design a collection of behaviors, which is used to explore the effect of different types of behaviors on authentication. This collection involves hand and finger behaviors since they are common and natural human actions; it also reasonably covers both basic movements and complex behaviors, two major categories of behaviors. We evaluated authentication performance along the dimensions of behaviors, users (sensitivity) and classification models (independence). A valuable finding shows that finger-issued movements present almost equivalent performance with complex behaviors. Therefore, it suggests applying them to the wearable and mobile devices as an alternative to complex behaviors, and could considerably reduce requirements for computational resources compared to complex behaviors. Furthermore, they extend the applications of behavioral authentication to a wider array of scenarios, like performing behavioral authentication such as moving fingers in one's pocket, which is common for wearable and mobile applications.

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Biometric verification using the motion of fingers: a combination of physical and behavioural biometrics

Cited by 11 publications

References 7 publications

Cancelable Biometric Identification by Combining Biological Data with Artifacts

Cancelable Biometric Identification by Combining Biological Data with Artifacts

Recent Application in Biometrics

Exploring the Hand and Finger-Issued Behaviors Toward Natural Authentication

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