Hand shape recognition

Lay, Yun Long

doi:10.1016/s0030-3992(99)00105-x

Cited by 23 publications

(16 citation statements)

References 7 publications

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“…The value of J is put to JCK. For every A i in (5), iZJKKC1,.,J, given in the form (9), if its level is greater than k, all the FREE nodes are again changed to nonterminal ones and we substitute…”

Section: Quadtree Representationsmentioning

confidence: 99%

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Quadtree-based representations of grid-oriented data

Vörös

2006

Image and Vision Computing

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Section: Quadtree Representationsmentioning

confidence: 99%

“…[4][5][6][7][8][9][10][11][12][13][14][15][16][17]). The quadtree representations decrease computer memory requirements and simplify algorithms in many applications dealing with two-dimensional (2D) pictures.…”

Section: Introductionmentioning

confidence: 99%

Quadtree-based representations of grid-oriented data

Vörös

2006

Image and Vision Computing

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“…Very little work has been performed in 3D hand biometrics. Lay [16] used a grating pattern projected on the back surface of the hand and its distortion by the handÕs shape as a biometric identifier. Our work represents the first to use the fine finger surface features such as skin folds and crease patterns extracted from dense range data as a biometric identifier.…”

Section: Introductionmentioning

confidence: 99%

Finger surface as a biometric identifier

Woodard

Flynn

2005

Computer Vision and Image Understanding

186

101

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“…Very little work has been performed in 3D hand biometrics. Lay [4] used a grating pattern projected on the back surface of the hand and its distortion by the hand's shape as a biometric identifier. Our work represents the first to use the fine finger surface features such as skin folds and crease patterns extracted from dense range data as a biometric identifier.…”

Section: Introductionmentioning

confidence: 99%

Identity Verification Utilizing Finger Surface Features

Flynn

2005

Lecture Notes in Computer Science

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Abstract. In this paper we present a unique approach to personal identification which utilized finger surface features as a biometric identifier. Finger surface features are extracted from dense range images of an individual's hand. The shape index (a curvature-based surface representation) is used to represent the surfaces of the index, middle, and ring fingers of an individual. This representation is used along with a correlation coefficient based matcher to determine similarity. Our experiments make use of data from 223 subjects possessing a 16 week time lapse between collections. We examine the performance of individual finger surfaces in a verification context as well as the performance when using the three finger surfaces in conjunction. We present the results of our experiments, which indicate that this approach performs well for a first-of-its-kind biometric technique.

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Hand shape recognition

Cited by 23 publications

References 7 publications

Quadtree-based representations of grid-oriented data

Quadtree-based representations of grid-oriented data

Finger surface as a biometric identifier

Identity Verification Utilizing Finger Surface Features

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