Human Identification by Vascular Patterns

Kosmala, Jan; Saeed, Khalid

doi:10.1007/978-1-4614-5608-7_4

Cited by 9 publications

(8 citation statements)

References 23 publications

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“…Unfortunately, as the sub-region matching method partitions an image into different non-overlapping blocks, it can not overcome the global variations such as whole translation or rotation between two images. Therefore, existing vein matching methods [ 3 – 14 , 16 , 17 , 19 ] cannot accommodate the local changes (local deformation) and global variations (global translation, and global rotation) at the same time. To overcome this drawback, we propose a new localized matching method based on the SIFT feature.…”

Section: Sub-region Matching Methodsmentioning

confidence: 99%

“…The existing finger-vein recognition methods [ 3 – 13 , 16 , 17 ] cannot employ the vein orientation pattern but rather use the vein shape pattern for identification. However, the vein pattern may not be effectively extracted due to the conditions of a sensor, the health conditions of humans, illumination variations and so on.…”

Section: Generating Fusion Matching Scoresmentioning

confidence: 99%

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Finger-Vein Verification Based on Multi-Features Fusion

Qin

Lan²,

Xue³

et al. 2013

Sensors

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This paper presents a new scheme to improve the performance of finger-vein identification systems. Firstly, a vein pattern extraction method to extract the finger-vein shape and orientation features is proposed. Secondly, to accommodate the potential local and global variations at the same time, a region-based matching scheme is investigated by employing the Scale Invariant Feature Transform (SIFT) matching method. Finally, the finger-vein shape, orientation and SIFT features are combined to further enhance the performance. The experimental results on databases of 426 and 170 fingers demonstrate the consistent superiority of the proposed approach.

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Section: Sub-region Matching Methodsmentioning

confidence: 99%

Section: Generating Fusion Matching Scoresmentioning

confidence: 99%

Finger-Vein Verification Based on Multi-Features Fusion

Qin

Lan²,

Xue³

et al. 2013

Sensors

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“…In previous research [ 7 ], they proposed the method of segmenting the finger area from the input image based on the gradient magnitude of spatial positions, and the method of extracting finger-vein lines based on the position-gray-profile curve. In another study [ 8 ], the authors proposed the finger-vein pattern-based human identification system where an image capturing device was made by a simple webcam and electronic circuit. In addition, the algorithms of finger-vein image denoising, binarization, and thinning were used for finger-vein recognition.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, the algorithms of finger-vein image denoising, binarization, and thinning were used for finger-vein recognition. Their methods showed good performance of extracting finger-vein patterns and recognition [ 7 , 8 ].…”

Section: Related Workmentioning

confidence: 99%

“…Typical biometric technologies include face [ 1 , 2 ], fingerprint [ 3 , 4 ], iris [ 5 , 6 ], and finger-vein recognition [ 7 , 8 ]. In other studies [ 9 ], authors proposed 3D facial recognition based on 105 novel geometrical descriptors generated by composing primary geometrical descriptors such as mean, Gaussian, principal curvatures, shape index, curvedness, and the coefficients of the fundamental forms, and by applying standard functions such as sine, cosine, and logarithm to them.…”

Section: Introductionmentioning

confidence: 99%

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Convolutional Neural Network-Based Finger-Vein Recognition Using NIR Image Sensors

Hong

Lee

Park

2017

Sensors

152

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Conventional finger-vein recognition systems perform recognition based on the finger-vein lines extracted from the input images or image enhancement, and texture feature extraction from the finger-vein images. In these cases, however, the inaccurate detection of finger-vein lines lowers the recognition accuracy. In the case of texture feature extraction, the developer must experimentally decide on a form of the optimal filter for extraction considering the characteristics of the image database. To address this problem, this research proposes a finger-vein recognition method that is robust to various database types and environmental changes based on the convolutional neural network (CNN). In the experiments using the two finger-vein databases constructed in this research and the SDUMLA-HMT finger-vein database, which is an open database, the method proposed in this research showed a better performance compared to the conventional methods.

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