Intensity Variation Normalization for Finger Vein Recognition Using Guided Filter Based Singe Scale Retinex

Xie, Shan Juan; Lu, Yu; Yoon, Sook; Ju, Yang; Park, Dong Sun

doi:10.3390/s150717089

Cited by 57 publications

(31 citation statements)

References 25 publications

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“…After extracting the ROI, the finger vein image is normalized in order to accommodate geometric changes and to get consistent image size [47]. In addition, normalization in the preprocessing stage eliminates the diverse variation problems of the image [48].…”

Section: Normalization and Enhancementmentioning

confidence: 99%

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%

A Systematic Review of Finger Vein Recognition Techniques

Han-gang²,

et al. 2018

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“…Alessandro Rizzi and his group have done much revolutionary research in the field of Retinex algorithms [11,12]. The fundamental of Retinex is to divide an image into two parts, the illumination image and the reflection image, and then to remove the illumination image to realize image enhancement [13,14]. Single-scale Retinex (SSR) and multi-scale Retinex (MSR) are two typical Retinex algorithms, and MSR is defined as a weighted sum of several SSRs [15].…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, many researchers have focused on the Retinex algorithm carefully and proposed some improvement strategies. Xie et al [14] used guided filter to improve SSR and got good results in enhancing the image quality and finger vein recognition accuracy. Xiao et al [16] employed a fast mean filtering to improve the performance of SSR.…”

Section: Introductionmentioning

confidence: 99%

Image Enhancement for Surveillance Video of Coal Mining Face Based on Single-Scale Retinex Algorithm Combined with Bilateral Filtering

Wang

et al. 2017

Symmetry

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Surveillance videos of coal mining faces have close relation to the safety of coal miners and mining efficiency. However, surveillance videos are always disturbed by some severe conditions such as atomization, low illumination, glare, and so on. Therefore, this paper proposed a hybrid algorithm (SSR-BF) based on the integration of single-scale Retinex (SSR) and bilateral filtering (BF) to enhance the image quality of surveillance videos. BF was coupled with SSR to reduce the noises and perfect the edge information in the image. The schematic diagram and pseudocode of SSR-BF was designed, and the parameters were set rationally to ensure the enhancement effects through some simulations. Finally, some comparisons with other methods were carried out, and the simulation results demonstrated that the proposed algorithm was superior to others and could be applied to image enhancement for poormonochrome images, especially the surveillance video of a coal mining face.

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“…To the best of our knowledge, all the listed CE turned out enhancing the final SIFT based vein recognition system performance in terms of EER. Carried with the confidence that performance will be improved [12–21] and with the reality that performance will be kept unchanged or declined [22–25], it is necessary to conduct comprehensive experiment to find out the specific influence of CE on SIFT based vein recognition system.…”

Section: Introductionmentioning

confidence: 99%

SIFT Based Vein Recognition Models: Analysis and Improvement

Wang

2017

Computational and Mathematical Methods in Medicine

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Scale-Invariant Feature Transform (SIFT) is being investigated more and more to realize a less-constrained hand vein recognition system. Contrast enhancement (CE), compensating for deficient dynamic range aspects, is a must for SIFT based framework to improve the performance. However, evidence of negative influence on SIFT matching brought by CE is analysed by our experiments. We bring evidence that the number of extracted keypoints resulting by gradient based detectors increases greatly with different CE methods, while on the other hand the matching result of extracted invariant descriptors is negatively influenced in terms of Precision-Recall (PR) and Equal Error Rate (EER). Rigorous experiments with state-of-the-art and other CE adopted in published SIFT based hand vein recognition system demonstrate the influence. What is more, an improved SIFT model by importing the kernel of RootSIFT and Mirror Match Strategy into a unified framework is proposed to make use of the positive keypoints change and make up for the negative influence brought by CE.

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Intensity Variation Normalization for Finger Vein Recognition Using Guided Filter Based Singe Scale Retinex

Cited by 57 publications

References 25 publications

A Systematic Review of Finger Vein Recognition Techniques

A Systematic Review of Finger Vein Recognition Techniques

Image Enhancement for Surveillance Video of Coal Mining Face Based on Single-Scale Retinex Algorithm Combined with Bilateral Filtering

SIFT Based Vein Recognition Models: Analysis and Improvement

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