ArcVein-Arccosine Center Loss for Finger Vein Verification

Hou, Borui; Yan, Ruqiang

doi:10.1109/tim.2021.3062164

Cited by 37 publications

(16 citation statements)

References 43 publications

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“…THU-FVFDT2 USM SDUMLA RLT [5] 96.72 86.79 83.49 LMC [6] 87.54 88.82 93.08 LDC [30] 95.25 87.05 88.37 PWDBC [50] 96.87 90.85 84.28 Hong et al [47] 95.08 78.66 90.41 Hou et al [14] 93.28 78.86 75.47 FV-GAN [15] 95.57 89.23 -MRFBCNN [46] 83.16 86.59 78.77 ArcVein [16] 98.85 89.02 86.01 SRC [17] 95.98 90.04 87.97 KCDVD [48] 74…”

Section: Methodsmentioning

confidence: 99%

“…Thirdly, MRFBCNN [46] outperforms other CNN-based methods, indicating the importance of higher-order statistics in the feature extraction of finger veins. Fourthly, the performance of the CNN-based methods [14]- [16], [47] is sometimes not as good as traditional methods. One reason is that, even with the data augmentation, the capacity of CNNs is limited by insufficient training data, especially in the single-sample protocol.…”

Section: Comparison Of Psrc With State-of-the-art 641 Finger Vein Ver...mentioning

confidence: 99%

“…FV-GAN was proposed in [15] by employing Generative Adversarial Networks (GAN) to extract binary vein patterns for recognition. By replacing Euclidean distance with cosine distance, Hou et al [16] proposed a new loss function named ArcVein for finger vein recognition.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Single-Sample Finger Vein Recognition via Competitive and Progressive Sparse Representation

Zhao

Chen

Xue

et al. 2023

IEEE Trans. Biom. Behav. Identity Sci.

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As an emerging biometric technology, finger vein recognition has attracted much attention in recent years. However, singlesample recognition is a practical and longstanding challenge in this field, referring to only one finger vein image per class in the training set. In single-sample finger vein recognition, the illumination variations under low contrast and the lack of information of intra-class variations severely affect the recognition performance. Despite of its high robustness against noise and illumination variations, sparse representation has rarely been explored for single-sample finger vein recognition. Therefore, in this paper, we focus on developing a new approach called Progressive Sparse Representation Classification (PSRC) to address the challenging issue of single-sample finger vein recognition. Firstly, as residual may become too large under the scenario of single-sample finger vein recognition, we propose a progressive strategy for representation refinement of SRC. Secondly, to adaptively optimize progressions, a progressive index called Max Energy Residual Index (MERI) is defined as the guidance. Furthermore, we extend PSRC to bimodal biometrics and propose a Competitive PSRC (C-PSRC) fusion approach. The C-PSRC creates more discriminative fused sample and fusion dictionary by comparing residual errors of different modalities. By comparing with several state-of-the-art methods on three finger vein benchmarks, the superiority of the proposed PSRC and C-PSRC is clearly demonstrated.

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Section: Methodsmentioning

confidence: 99%

Section: Comparison Of Psrc With State-of-the-art 641 Finger Vein Ver...mentioning

confidence: 99%

See 1 more Smart Citation

Single-Sample Finger Vein Recognition via Competitive and Progressive Sparse Representation

Zhao

Chen

Xue

et al. 2023

IEEE Trans. Biom. Behav. Identity Sci.

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“…Xie et al [24] combined CNN and supervised discrete hashing to propose a new finger vein recognition method that significantly reduces the template size. Hou and Yan [25] proposed an arccosine center loss function that significantly improves the feature recognition capability of CNN, but this method is time-consuming and ineffective in feature extraction. Liu et al [26] designed a shallow network with three convolutional blocks and two fully connected layers, which can be effectively applied to both closed-set architecture (CSarchitecture) and open-set architecture (OS-architecture).…”

Section: A Related Workmentioning

confidence: 99%

“…Like the difficulty of face recognition in dealing with twins, when there are multiple finger vein skeletons closer to each other in the training data, the possibility of discriminative error will be high, so a lot of experiments are still needed to judge the feasibility and effectiveness of the method proposed in this paper on finger vein recognition, and if the effect of a single model is poor, the idea of Ensemble Learning will be one of the directions of subsequent optimization. [23] 93.62 11.66 CNN-CO [35] -2.37 CNN(triplet loss) [24] 96.16 7.03 CNN(center loss) [36] 92.07 15.99 TWO-STREAM CNN [37] -0.47 VEIN-CNN [31] -3.42 Arccosine center loss [25] 98.86 2.60 Arccosine center loss + softmax loss [25] 97.52 2.15 proposed method 98.40 2.8…”

Section: F Performance Comparison and Disscussionmentioning

confidence: 99%

An Optimization Algorithm to Improve the Accuracy of Finger Vein Recognition

et al. 2022

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As people's daily behavioral activities become more data-based, how to protect personal information security is a key consideration for the whole society. Finger vein recognition is becoming an important means of identification because of its uniqueness, live detection, security and many other advantages. Although deep learning can make finger vein recognition have good effect, but the number of samples needed to build deep network model is too large, and the current authoritative finger vein database cannot reach the minimum number of samples required. The emergence of Muti-Grained Cascade Forest provides a solution to the problem of insufficient sample data and long training time, which can give a new research avenue in feature extraction. In order to obtain higher accuracy, the deep forest algorithm is introduced in this paper to process the finger vein images. Firstly, the image data in the finger vein image database is pre-processed to prepare for the subsequent feature extraction and matching. Then, the deep forest algorithm is used to find the feature points and ORB algorithm is used to match the features to obtain the angular information of each matched pair, and the final identity is determined according to the sparse distribution of angles. The accuracy of finger vein recognition based on deep forest algorithm is 98.40%. By comparing with other machine learning methods for finger vein recognition, the proposed method has a higher accuracy rate.

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