Finger vein recognition: utilization of adaptive gabor filters in the enhancement stage combined with SIFT/SURF-based feature extraction

Kovač, Ivan; Marák, Pavol

doi:10.1007/s11760-022-02270-8

Cited by 19 publications

(12 citation statements)

References 12 publications

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“…Besides, the LBP [7] and the improved versions [8] are investigated to vein feature extraction.The experimental results show that the proposed approach can effectively improve the performance of finger vein recognition. Kova [9] investigates an adaptive Gabor filter based vein extraction approach.…”

Section: Related Workmentioning

confidence: 99%

VeinCnnformer: convolutional neural network based transformer for vein recognition

wang,

Qin,

Zhang

et al. 2024

Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023)

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Vein recognition has received increasing attention in recent years. Currently, the deep learning such transformer show robust capacity of feature representation and successfully applied for vein recognition task. Despite of its advances in vein recognition, but it may fail to capture the 2D structure and spatial local information within each patch for image-based vision tasks. In this paper, we propose a novel Convolutional neural network based transformer for vein recognition, termed VeinCnnformer to take advantage of convolutional operations and self-attention mechanisms for improving representation learning. Our VeinCnnformer roots in the Mixed Convolutional Attention Module (MCAM), which fuses local features and global representations. Specifically, we propose three modules, convolutional neural network(CNN) module, convolutional channel attention module, and transformer module. Secondly, the convolutional channel attention module and CNN module are combined with transformer module to form the MCAM. Thirdly, we stack multiple MCAM to obtain our VeinCnnformer for vein recognition. Finally, the experimental results on three public vein databases show that our VeinCnnformer outperforms the existing vein recognition approaches and achieves the state of the art recognition results.

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Section: Related Workmentioning

confidence: 99%

VeinCnnformer: convolutional neural network based transformer for vein recognition

wang,

Qin,

Zhang

et al. 2024

Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023)

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show abstract

“…Some commonly used traditional algorithms were developed from the following techniques: (1) Vein pattern: It needs to extract the topology of the image [7, 8], which is highly influenced by the image quality, such as Gabor filtering [9], maximum curvature (MC) [10] and repetitive linear tracking (RLT) [11]. (2) Feature points: the similarity between images is calculated by extracting representative points [12, 13], but such schemes are poorly realized due to the presence of spurious points [14].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Finger vein recognition based on lightweight convolutional attention model

Zhang

Wang

2023

IET Image Processing

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Convolutional neural networks are a hot research topic in finger vein recognition. However, most existing research focuses on increasing the depth and width of the convolutional neural network to improve the network's performance, which has a specific requirement on the computer's computational power. To reduce the computational burden, a lightweight convolutional attention model (LCAModel) is proposed for finger vein recognition to achieve more accurate visual structure capture by exploiting the sensitivity of the attention mechanism to features. First, the attentional model (AModel) is proposed to extract representative features of images, which mainly utilizes the adaptive mapping on space and channels of the convolutional block attention module (CBAM) to make features distinguishable. In addition, considering the integrity of the features, a convolutional model (CModel) is designed to supplement the features of AModel. Finally, the features obtained from AModel and CModel are fused using an adaptive weighting mechanism to make the features more complete. Here, the obtained features are provided into a support vector machine (SVM) for image classification. The experiments are carried out on two publicly available databases, demonstrating that the proposed network structure requires less computing power and performs better.

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“…But recognition rate is low, when identifying noisy objects. SIFT [8] is used as a local feature for object recognition. The stability of the algorithm is good, but the recognition speed is slow with a great deal of computing time.…”

mentioning

confidence: 99%

“…Experimental results and analysis: In this study, in order to verify the robustness of the proposed method, the two image libraries (COIL-100 and SOIL-47) are respectively used for object recognition based on shape [7], interest point [8], shape and SIFT [9], our SIPD in our experiments.…”

mentioning

confidence: 99%

Object recognition based on shape interest points descriptor

Zhang,

2024

Electronics Letters

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Considering the defect of object recognition with single global feature or local feature, in this letter the authors propose a shape interest points descriptor (SIPD) for object recognition. Particularly, the authors extract the shape features by the improved HU moments and then interest points by Speeded up Robust Feature (SURF). Object recognition is carried out by similarity measure. Because the influence of the improved HU moments and SURF for object recognition is different, the two different similarity measures are fused effectively by using different weight factors. Experimental results show that the author’ proposed method is effective and robust to the changes of scale, viewing angle, illumination and noise when compared with the other representative methods.

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Finger vein recognition: utilization of adaptive gabor filters in the enhancement stage combined with SIFT/SURF-based feature extraction

Cited by 19 publications

References 12 publications

VeinCnnformer: convolutional neural network based transformer for vein recognition

VeinCnnformer: convolutional neural network based transformer for vein recognition

Finger vein recognition based on lightweight convolutional attention model

Object recognition based on shape interest points descriptor

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