Thermographic imaging of the subcutaneous vascular network of the back of the hand for biometric identification

Cross, Jennifer; Smith, Clifton L.

doi:10.1109/ccst.1995.524729

Cited by 106 publications

(69 citation statements)

References 8 publications

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“…Such near IR imaging of hand vein patterns have also been investigated in the literature for personal identification. Cross and Smith [3] have detailed the usage of near IR imaging for the extraction of hand vein patterns. Authors have demonstrated the two-fold matching of medial axis representation, following the vein skeleton extraction, for the authentication of 20 users.…”

Section: Prior Workmentioning

confidence: 99%

“…The acquisition of hand vein images using near IR imaging has been studied in [3], [11], [15]. In this work, a low-cost near IR camera, traditionally used for surveillance, was employed for the contactless image acquisition.…”

Section: Image Acquisitionmentioning

confidence: 99%

“…Therefore the researchers [4]- [5], [8], [10] have focused on the solutions using near IR imaging. The imaging setup in prior work [3]- [4], [8], [13]- [14] employed hand docking frame device (to restrict the translational and rotational changes) which are often inconvenient and not user friendly. Furthermore, with the notable exception of [1], [3], there is no systematic (completely automated) proposal for hand vein based user authentication.…”

Section: Prior Workmentioning

confidence: 99%

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Personal Authentication Using Hand Vein Triangulation and Knuckle Shape

Kumar

Kanakam

2009

IEEE Trans. on Image Process.

266

122

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IntroductionThe rapid growth in the use of e-commerce applications requires reliable user identification for effective and secure access control. The face identification beyond visible spectrum is increasing receiving attention and has highest user acceptance [23], [7]. Hand vein identification has emerged as a promising component of biometrics study.The subcutaneous vascular pattern/network appearing on the back of hand, referred to as the hand vein in this paper, is extremely difficult to forge and therefore offers promising These changes in vascular system make the vein pattern loose and change its size as compared to earlier. As the vascular system is a large and essential system of the body, it is largely affected due to any change in body; either by nature or by disease. The diabetes, hypertension, atherosclerosis, metabolic disorders [25] and tumors [26] are some diseases which affect the vascular systems and made it thick or thin. There have been several other efforts to investigate the utility of hand vein patterns for effective user authentication. In the following section, we present a brief review on the related prior work which is followed by the salient features of the proposed approach investigated in this paper. Proposed SystemIn this paper, we develop a new hand vein authentication approach utilizing the structural similarity of hand vein triangulation and knuckle shape features. The block diagram of the proposed approach is shown in figure 2. The approach has been adapted to utilize palm dorsal images acquired from the, low-cost, contactless, near IR imaging. The main contributions from this paper [33] can be summarized as follows:(i) In this paper, we investigate the extraction and matching of hand vein structure using the key point triangulation. It many be noted that the low quality (visibility and clarity) of vein images does not guarantee same number of key points (from the same user) and therefore a weighted combination of four different types of triangulation is developed. Further details of this strategy can be seen section VII.(ii) The proposed method also investigates the utility of knuckle shape features since these features can be simultaneously extracted from the acquired images. The matching The image contours extracted from the acquired images are used for the image normalization and segmentation of region of interest (ROI) which is detailed in section II-IV. The automated extraction of hand vein map from ROI images is described in section V. The extraction and triangulation of local key points from the hand vain map is detailed in section VI and section VII respectively. The hierarchical matching scheme for the triplets is introduced in section VIII. The experiments and results from this work are presented in section IX which is followed by the discussion in section X and the main conclusions from this paper are summarized section XI. II. Image AcquisitionThe acquisition of hand vein images using near IR imaging has been studied in [3], [11], [15]. In this work, a low-cost n...

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Section: Prior Workmentioning

confidence: 99%

Section: Image Acquisitionmentioning

confidence: 99%

Section: Prior Workmentioning

confidence: 99%

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Personal Authentication Using Hand Vein Triangulation and Knuckle Shape

Kumar

Kanakam

2009

IEEE Trans. on Image Process.

266

122

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“…Cross, C.L. Smith [1] proposed a system based on low cost automatic thermo graphic imaging system. Matching can be done by comparing a given signature against a single template or library of templates.…”

Section: Previous Workmentioning

confidence: 99%

Analysis of Vascular Pattern Recognition Using Neural Network

Kaur¹,

Singh²

2015

IJMSC

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Biometric identification using vein patterns is a recent technique. The vein patterns are unique to each individual even in twins and they don"t change over age except their size. As veins are beneath the skin it is difficult to forge. BOSPHOROUS hand vein database is used in this work. Hand vein images are uploaded first and key points using Scale Invariant Feature Transform (SIFT) are extracted. Then the neural network is used for training these images. Finally neural network is used for testing these images to check whether the image used for testing matches with the existing database or not. Results are computed like False Acceptation Rate (FAR), False Rejection Rate (FRR), accuracy and error per bit stream.

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“…With vein patterns represented by their medial axes and employing constrained sequential correlation, Cross and Smith (1995) achieved a False Rejection Rate (FRR) of 7.5% and a False Acceptance Rate (FAR) of 0% based on 100 images from 20 subjects. With vein patterns represented by their minutiae points and employing the Hausdorff distance, Wang et al (2008) showed the possibility of achieving an FRR and an FAR of 0% for 47 subjects.…”

Section: Introductionmentioning

confidence: 99%

Improving hand vein recognition by score weighted fusion of wavelet-domain multi-radius local binary patterns

Huang

Shark

Wang

et al. 2016

IJCAT

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Among biometric modalities, hand vein patterns are seen as providing an attractive method for high-level security access applications owing to high impenetrability and good user convenience. For biometric recognition based on near-infrared dorsal hand vein images, Local Binary Patterns (LBP) have emerged as a highly effective descriptor of local image texture with high recognition performance reported. In this paper, the traditional approach with LBP applied in the spatial domain is extended to multi-radius LBP in the wavelet domain to provide a more comprehensive set of feature categories to capture grey-level variation characteristics of vein patterns, and score weighted fusion based on the relative discriminative power of each feature category is proposed to achieve higher recognition performance. The proposed methodology is shown to provide a more robust performance with a recognition rate in excess of 99% and an equal error rate significantly less than 2%.

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Thermographic imaging of the subcutaneous vascular network of the back of the hand for biometric identification

Cited by 106 publications

References 8 publications

Personal Authentication Using Hand Vein Triangulation and Knuckle Shape

Personal Authentication Using Hand Vein Triangulation and Knuckle Shape

Analysis of Vascular Pattern Recognition Using Neural Network

Improving hand vein recognition by score weighted fusion of wavelet-domain multi-radius local binary patterns

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