Finger Vein Image Inpainting With Gabor Texture Constraints

Yang, Huaidong; Shen, Lianfeng; Yao, Yu Dong; Wang, Huaxia; Zhao, Guodong

doi:10.1109/access.2020.2990966

Cited by 6 publications

(5 citation statements)

References 32 publications

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“…Thus, the influence of such types of noises is not considered in this study. There have been some methods proposed recently [20][21] to remove noises from the captured finger vein images.…”

Section: B Modelling the Global Blurrinessmentioning

confidence: 99%

Finger Vein Image Deblurring Using Neighbors-Based Binary-GAN (NB-GAN)

Shen

Yao

et al. 2023

IEEE Trans. Emerg. Top. Comput. Intell.

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Vein contraction and venous compression typically caused by low temperature and excessive placement pressure can blur the captured finger vein images and severely impair the quality of extracted features. To improve the quality of captured finger vein image, this paper proposes a 26-layer generator network constrained by Neighbors-based Binary Patterns (NBP) texture loss to recover the clear image (guessing the original clear image). Firstly, by analyzing various types and degrees of blurred finger vein images captured in real application scenarios, a method to mathematically model the local and global blurriness using a pair of defocused and mean blur kernels is proposed. By iteratively and alternatively convoluting clear images with both kernels in a multi-scale window, a polymorphic blur training set is constructed for network training. Then, NBP texture loss is used for training the generator to enhance the deblurring ability of the network on images. Lastly, a novel network structure is proposed to retain more vein texture feature information, and two residual connections are added on both sides of the residual module of the 26-layer generator network to prevent degradation and overfitting. Theoretical analysis and simulation results show that the proposed neighbors-based binary-GAN (NB-GAN) can achieve better deblurring performance than the the-state-of-theart approaches.

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“…Thus, the influence of such types of noises is not considered in this study. There have been some methods proposed recently [20][21] to remove noises from the captured finger vein images.…”

Section: B Modelling the Global Blurrinessmentioning

confidence: 99%

Finger Vein Image Deblurring Using Neighbors-Based Binary-GAN (NB-GAN)

Shen

Yao

et al. 2023

IEEE Trans. Emerg. Top. Comput. Intell.

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“…In this study, we designed DeblurGAN-v2 with FPN as backbone architecture and obtained final feature maps at five different scales as output. These feature maps are further unsampled to 1 4 of the input size and concatenated into a tensor containing different levels of finger vein feature information. Moreover, in pursuit of the strong deblurring performance and the reduction in the loss of vein texture information during feature extraction by the network, we used Inception-Resnet-v2 as the backbone and collaborated with FPN to form the generative network.…”

Section: The Deblurgan-v2 Generator Architecture and Loss Functionmentioning

confidence: 99%

“…With the growing concern for information security, biometric technology has been recognized as one of the most reliable security technologies in the field of information security [1]. Compared to other biometric technologies, finger vein recognition has unique advantages, such as low cost, non-contact acquisition, and uniqueness of finger vein information, especially in the current world epidemic situation, its contactless collection method has been widely accepted [2].…”

Section: Introductionmentioning

confidence: 99%

“…FMM improves time efficiency but blindly uses all known information in the patch for the weighting calculation, resulting in vein textures that do not consider texture constraints and do not accurately characterize the texture. Yang H et al [1] proposed a restoration algorithm based on Gabor texture feature constraints. During the restoration process, the information of the neighboring blocks is selectively weighted according to the features of similar textures in the local region, making the vein texture of the restored image more coherent.…”

Section: Introductionmentioning

confidence: 99%

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DRL-FVRestore: An Adaptive Selection and Restoration Method for Finger Vein Images Based on Deep Reinforcement

Gao

Al-Azzawi

et al. 2023

Applied Sciences

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Finger vein recognition has become a research hotspot in the field of biometrics due to its advantages of non-contact acquisition, unique information, and difficulty in terms of forging or pirating. However, in the real-world application process, the extraction of image features for the biometric remains a significant challenge when the captured finger vein images suffer from blur, noise, or missing feature information. To address the above challenges, we propose a novel deep reinforcement learning-based finger vein image recovery method, DRL-FVRestore, which trained an agent that adaptively selects the appropriate restoration behavior according to the state of the finger vein image, enabling continuous restoration of the image. The behaviors of image restoration are divided into three tasks: deblurring restoration, defect restoration, and denoising and enhancement restoration. Specifically, a DeblurGAN-v2 based on the Inception-Resnet-v2 backbone is proposed to achieve deblurring restoration of finger vein images. A finger vein feature-guided restoration network is proposed to achieve defect image restoration. The DRL-FVRestore is proposed to deal with multi-image problems in complex situations. In this paper, extensive experimental results are conducted based on using four publicly accessible datasets. The experimental results show that for restoration with single image problems, the EER values of the deblurring network and damage restoration network are reduced by an average of 4.31% and 1.71%, respectively, compared to other methods. For images with multiple vision problems, the EER value of the proposed DRL-FVRestore is reduced by an average of 3.98%.

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“…In these methods, it is effective to analyze the surface features of objects by the spatial dependence of gray level. The Tamura texture [16] analysis method is effective to analyze the contrast, coarseness and directionality of the image, but it is poor in recognition of the linelikeness, regularity and roughness of the image. The algorithm of extracting texture feature based on Gabor [17] wavelet transform has better retrieval effect in frequency domain, and it can eliminate redundant information.…”

Section: A Shortcoming Of Extracting Texture Algorithmsmentioning

confidence: 99%

A Method for Identifying Flaws on Product Surface Based on Spatial Connectivity Domain

et al. 2021

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In this paper, we exploit a method for identifying flaws on product surface based on spatial connectivity domain. A number of algorithms for detecting local features exist that were established to enhance the efficiency and accuracy of identifying interest features, such as AKAZE , BFSIFT , BRIEF , BRISK , ORB , SURF , SIFT and PCA-SIFT algorithm. But the data of flaws on product surface which is similar and consistent with the background intensity became a dilemma to detect the feature of image. In terms of identifying flaws on product surface, the above algorithms are not effective and accurate. Our aim is to enhance the accuracy of detecting the feature of flaws on product surface, so that the product with flaws could be accurately identified in industrial production. Therefore, we propose a method to identify flaws on product surface based on spatial connectivity domain. Compared with some other algorithms, the proposed method is more effective and accurate in detecting the local feature flaws on product surface of auto parts in automotive manufacturing factory.

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Finger Vein Image Inpainting With Gabor Texture Constraints

Cited by 6 publications

References 32 publications

Finger Vein Image Deblurring Using Neighbors-Based Binary-GAN (NB-GAN)

Finger Vein Image Deblurring Using Neighbors-Based Binary-GAN (NB-GAN)

DRL-FVRestore: An Adaptive Selection and Restoration Method for Finger Vein Images Based on Deep Reinforcement

A Method for Identifying Flaws on Product Surface Based on Spatial Connectivity Domain

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