Robust and adaptive ROI extraction for hyperspectral dorsal hand vein images

Nie, Wei; Zhang, Bob

doi:10.1049/iet-cvi.2018.5732

Cited by 7 publications

(2 citation statements)

References 38 publications

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“…2). These low intensity values cause the output of the image difference to be of negative value when α = 1: R(i r , j r ) < B(i r , j r ) => R(i r , j r ) − B(i r , j r ) < 0, (7) where (i r , j r ) denote the pixel coordinates of the palm-vein line. On the another hand, since the intensity of the non-vein regions for the two channels are quite similar (blue circle in Fig.…”

Section: A Palmprint Suppressionmentioning

confidence: 99%

“…This is because the hand-based biometrics are highly practical for daily applications in view of their feature stability, ease of collection and usage convenience [4]. The hand biometrics consist of dorsal-vein [5]- [7], finger-vein [8]- [10], palmvein [11]- [22], palmprint [23]- [36], fingerprint [37]- [39], inner-knuckle print [40]- [42], and hand-geometry [43]- [45]. Fingerprint, palmprint and inner-knuckle-print are widely utilized for user authentication, but these modalities are sensitive to external imaging conditions such as illumination change and noise on the hand (e.g., moisture, dust etc.).…”

Section: Introductionmentioning

confidence: 99%

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Palm-Vein Verification Using Images From the Visible Spectrum

Cho

Kim

et al. 2021

IEEE Access

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In this paper, we investigate into utilization of images from the visible light (RGB) spectrum for identity verification based on the palm-veins. This is differentiated from the commonly utilized Nearinfrared (NIR) images for palm-vein feature extraction. Our goal is to explore into the often omitted palm-vein information from the RGB palm images considering the vast deployment of the RGB cameras. Essentially, the vein line features are extracted at various scales based on an efficient difference image projection. The extracted features from the gallery and the probe images are matched based on a modified Hamming distance measure. The resultant similarity scores are finally fused at score level for accuracy enhancement. Experiments are conducted on two public multi-spectral palm databases. The results show encouraging matching accuracy and computational efficiency of the proposed method which extracts the palm-vein utilizing only the visible spectrum. The outcome of this study can be deployed as a standalone biometric or as part of a multibiometric system for secured authentication.

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Section: A Palmprint Suppressionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Palm-Vein Verification Using Images From the Visible Spectrum

Cho

Kim

et al. 2021

IEEE Access

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A deep biometric hash learning framework for three advanced hand‐based biometrics

Shao

Zhong

2021

IET Biometrics

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Hand‐based biometrics has undergone extensive research in recent decades. Besides fingerprint, which is commonly used in personal authentication, there are other three advanced hand‐based biometrics that are further researched worldwide, such as palmprint, palm vein, and dorsal hand vein. However, the academics mainly focus on their unimodal or multimodal recognitions, and few researchers conduct comprehensive comparisons for them to guide practical applications, that is, to assess which one is the suitable biometric modality. Inspired by deep hashing network (DHN) and transfer learning, we propose a deep biometric hash learning (DBHL) framework to uniformly analyse and deal with these three biometrics. An end‐to‐end network is involved to convert images into binary codes. Pre‐trained network is employed for fine‐tuning, and the hamming distance is adopted to measure the similarity between the codes of query and registration images. Through experiments on benchmarks, equal error rate (EER) and the maximum or minimum distance of genuines or imposters are obtained as performance metrics. In experiments, EERs of palmprint and palm vein recognitions reach 0%, and EER of dorsal hand vein recognition is as low as 0.196%. It shows that the DBHL framework is effective to construct hand‐based biometric systems. Furthermore, a plain comparison among them shows that palmprint is more suitable in practical applications.

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A Recognition System for Partially Occluded Dorsal Hand Vein Using Improved Biometric Graph Matching

et al. 2020

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Some portions of dorsal hand may be occluded due to injuries, pigmentation, or tattoos, which significantly affects the performance of dorsal hand vein recognition systems. Biometric graph matching is a common shape-based feature extraction algorithm for vein recognition. However, this method does not consider edge attributes, which can provide additional discrimination ability. We present an improved biometric graph matching method that includes edge attributes for graph registration and a matching module to extract discriminating features. Moreover, we propose a recognition system for partially occluded dorsal hand vein. A database of normal hand vein images, three databases of images with artificially occluded dorsal hand vein with occlusions in different positions and ratios, and a database of images with tattooed hands are established to verify the validity of the proposed method. The experimental results demonstrated that the equal error rates and the accuracies were 0.0202 and 98.09% ± 0.28%, respectively for the normal hand vein images, 0.0453 and 96.58% ± 0.34%, respectively for images of artificially occluded dorsal hand vein with occlusion at all positions and area ratios (0 − 20%, mean occluded area ratio = 9.3%), and 0.0343 and 97.14% ± 0.29%, respectively for the images of tattooed hands. INDEX TERMS Dorsal hand vein recognition, biometric graph matching, occlusion, databases.

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Robust and adaptive ROI extraction for hyperspectral dorsal hand vein images

Cited by 7 publications

References 38 publications

Palm-Vein Verification Using Images From the Visible Spectrum

Palm-Vein Verification Using Images From the Visible Spectrum

A deep biometric hash learning framework for three advanced hand‐based biometrics

A Recognition System for Partially Occluded Dorsal Hand Vein Using Improved Biometric Graph Matching

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