Exploring finger vein based personal authentication for secure IoT

Lu, Yu; Wu, Shiqian; Fang, Zhijun; Xiong, Naixue; Yoon, Sook; Park, Dong Sun

doi:10.1016/j.future.2017.07.013

Cited by 90 publications

(59 citation statements)

References 40 publications

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“…Adaptive histogram equalization enhancement was implemented by Reference [10] to detect edges of vein significantly and enhanced the global contrast of input image. In References [5,25,31,48,53], Gabor filter technique was used to enhance finger vein images and remove noise from images. Zhang et al applied CGF for image enhancement of finger vein [54].…”

Section: Normalization and Enhancementmentioning

confidence: 99%

“…For instance, the retinal surface is affected by the high intensity of light during extraction of iris features [4]. Similarly, the accuracy of face identification is also distorted due to brightness variance, style of facial, blockage of blood vein and pose [5]. Table 1 present the merits, defects and other characteristics of some typical extrinsic and intrinsic features.…”

Section: Introductionmentioning

confidence: 99%

“…Vein-based systems usually utilize various anatomic features like finger vein, hand vein, foot vein or palm vein for personal verification. Finger vein is preferable in that its imaging tool is smallest, and the fact that fingers have a larger number of veins than the palm and hand [5]. In addition, a every finger vein pattern is unique even for identical twins and exists only for live humans [9].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Systematic Review of Finger Vein Recognition Techniques

Han-gang²,

et al. 2018

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Biometric identification is the study of physiological and behavioral attributes of an individual to overcome security problems. Finger vein recognition is a biometric technique used to analyze finger vein patterns of persons for proper authentication. This paper presents a detailed review on finger vein recognition algorithms. Such tools include image acquisition, preprocessing, feature extraction and matching methods to extract and analyze object patterns. In addition, we list some novel findings after the critical comparative analysis of the highlighted techniques. The comparative studies indicate that the accuracy of finger vein identification methods is up to the mark.

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Section: Normalization and Enhancementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Systematic Review of Finger Vein Recognition Techniques

Han-gang²,

et al. 2018

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show abstract

“…For example, during the extraction of iris features, the surface of retina may be affected by transmitting high intensity light [5]. Similarly, distortion can occur in face identification due to various factors such as brightness, variance, and pose [6]. In 2002, [7] introduced finger vein recognition technique [8].…”

Section: Introductionmentioning

confidence: 99%

“…For personal verification, systems based on veins generally use diverse anatomic features like finger vein, hand vein, foot vein or palm vein. The most recommended is finger vein because it has the smallest imaging tool and has larger number of veins as compared to palm and hand [6]. Additionally, even for the identical twins, vein pattern for every finger is unique and exists only for humans as long as they are alive [11].…”

Section: Introductionmentioning

confidence: 99%

Finger vein recognition using two parallel enhancement ppproachs based fuzzy histogram equalization

Jumaa¹,

Zidan²

2019

PEN

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This paper evaluates a set of enhancement stages for finger vein enhancement that not only has low computational complexity but also high distinguishing power. This proposed set of enhancement stages is centered around fuzzy histogram equalization. Two sets of evaluation are carried out: one with the proposed approach and one with another unique approach that was formulated by rearranging and cropping down the preprocessing steps of the original proposed approach. To extract features, a combination of Hierarchical Centroid and Histogram of Gradients was used. Both enhancement stages were evaluated with K Nearest Neighbor and Deep Neural Networks using 6 fold stratified cross validation. Results showed improvement as compared to three latest benchmarks in this field that used 6-fold validation.

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Modular‐based secret image sharing in Internet of Things: A global progressive‐enabled approach

Zhang

Zheng

et al. 2020

Concurrency and Computation

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Summary Due to the continuous development and progress of information technology, the Internet has also entered the era of big data based on the Internet of Things (IoT). How to protect the security of data stored and transmitted in the IoT is one of the urgent problems to be solved. This article focuses on the security issues of storage and transmission of image data in the IoT. Secret image sharing (SIS) is a kind of image protection mechanism by dividing an image into n shares, and different shares are given to different participants separately for preservation. Only when the number of shares reaches the threshold can the original image be recovered. From the perspective of image reconstruction mode, there are two types of SIS schemes: one is the traditional (k, n) threshold scheme, which provides an all‐or‐nothing reconstruction mode, the other is the progressive scheme, which can gradually restore the original image. In this article, a novel (k, k2) progressive secret image sharing based on modular operations is proposed, this method can divide the important images stored in the IoT into many parts and then transmit them to people in different places. It takes the whole as a unit in terms of the progressive recovery form. When the share reaches the threshold, certain blocks of the original image can be seen. As the share increases, the image will be clearer. When all shares participate in the reconstruction together, the original image can be restored without loss. Compared with other schemes, our scheme has the same smoothness, shadow size and satisfies the security, and is fine‐grained progressive.

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Exploring finger vein based personal authentication for secure IoT

Cited by 90 publications

References 40 publications

A Systematic Review of Finger Vein Recognition Techniques

A Systematic Review of Finger Vein Recognition Techniques

Finger vein recognition using two parallel enhancement ppproachs based fuzzy histogram equalization

Modular‐based secret image sharing in Internet of Things: A global progressive‐enabled approach

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