The biometric recognition system based on near-infrared finger vein image

Ma, Hui; Hu, Na; Fang, Chunxin

doi:10.1016/j.infrared.2021.103734

Cited by 27 publications

(11 citation statements)

References 33 publications

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“…The proposed method achieves 99.67% accuracy, better than other novel methods. Second, Table 6 shows that the FFV-MBC method has lower EER than EMC [38], lightweight CNN [39], MULBP + Block (2D) 2 PCA [40], PLPQ [41], and FVRAS-Net [42]. Experiments on SDUMLA-HMT.…”

Section: Resultsmentioning

confidence: 99%

FFV-MBC: A Novel Fused Finger-Vein Recognition Method Based on Monogenic Binary Coding

Tao¹,

Tao²,

Wang³

et al. 2023

Journal of Compurters

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<p>To improve pattern representation capabilities and robustness in traditional finger-vein recognition algorithms. In this paper, we propose FFV-MBC, a novel fused finger-vein recognition method based on monogenic binary coding (MBC). First of all, the amplitude, orientation, and phase information of the finger-vein images are filtered by a multi-scale monogenic log-Gabor filter and encoded by the binary coding theory. Three local features, MBC-A, MBC-P, and MBC-O, are achieved from different combinations of local image intensity and variation coding. After obtaining the features, we utilize the block-based Fisher Linear Discriminant method to reduce the dimension. Finally, the similarity components are calculated by the cosine distance and fused for the final finger-vein recognition results. We evaluate our proposed method on two publicly available datasets and one self-built dataset, i.e., Malaysian Polytechnic University (FV-USM), the Group of Machine Learning and Applications of Shandong University (SDUMLA-HMT), and our team, Signal and Information Processing Laboratory (FV-SIPL). On average, the proposed method achieved high recognition accuracy, i.e., 99.30%, and 1.10% equal error rates (EER). Overall, the proposed method performs better than most classical and state-of-the-art finger-vein recognition methods.</p> <p> </p>

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Section: Resultsmentioning

confidence: 99%

FFV-MBC: A Novel Fused Finger-Vein Recognition Method Based on Monogenic Binary Coding

Tao¹,

Tao²,

Wang³

et al. 2023

Journal of Compurters

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“…This technique can only identify a single feature of an image by its face. As human beings continue to generate comprehensive needs for image features, a single image feature recognition method can no longer meet people's needs [ 12 ]. People need more comprehensive image feature recognition technology to explore more comprehensive image information and obtain various information contained in images more comprehensively and accurately [ 13 ].…”

Section: Research Theories and Methodsmentioning

confidence: 99%

Feature Recognition and Style Transfer of Painting Image Using Lightweight Deep Learning

Tan

2022

Computational Intelligence and Neuroscience

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This work aims to improve the feature recognition efficiency of painting images, optimize the style transfer effect of painting images, and save the cost of computer work. First, the theoretical knowledge of painting image recognition and painting style transfer is discussed. Then, lightweight deep learning techniques and their application principles are introduced. Finally, faster convolutional neural network (Faster-CNN) image feature recognition and style transfer models are designed based on a lightweight deep learning model. The model performance is comprehensively evaluated. The research results show that the designed Faster-CNN model has the highest average recognition efficiency of about 28 ms and the lowest of 17.5 ms in terms of feature recognition of painting images. The accuracy of the Faster-CNN model for image feature recognition is about 97% at the highest and 95% at the lowest. Finally, the designed Faster-CNN model can perform style recognition transfer on a variety of painting images. In terms of style recognition transfer efficiency, the highest recognition transfer rate of the designed Faster-CNN model is about 79%, and the lowest is about 77%. This work not only provides an important technical reference for feature recognition and style transfer of painting images but also contributes to the development of lightweight deep learning techniques.

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“…The frequency-domain feature extraction method uses some linear transformation or filters to convert finger vein images to the transform domain and applies some energy criterion to extract features. Ma et al (2021), based on the encoding of discriminative information such as the orientation and scale of images in the frequency domain, proposed introducing directional gradients and local phase quantization to construct a discriminative pyramid histogram for finger vein recognition. Du et al (2018) introduced the real-valued discrete Gabor transform to transform the extracted features into the frequency domain and then extracted the texture features of finger veins in the view of space-frequency analysis.…”

Section: Related Workmentioning

confidence: 99%

Finger Vein Recognition Based on a Histogram of Competitive Gabor Directional Binary Statistics

Wang

et al. 2023

Journal of Database Management

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Traditional methods of extracting finger vein texture changes and orientation features are susceptible to illumination, translation, noise, and rotation, and the process has difficulty directly extracting structural features through the original image. In this paper, the histogram of competitive Gabor directional binary statistics (HCGDBS) is proposed to extract discriminative structural features. First, the index of the largest filter value is obtained based on the multidirectional Gabor filter as the dominant direction, thereby obtaining the rotation-invariance feature. Second, according to the filter response size of each pixel in different directions, the order difference relationship between the adjacent three directions is compared, and a highly discriminative competitive Gabor direction binary pattern (CGDBP) is constructed. Finally, the CGDBP features are extracted in blocks, and the HCGDBS is constructed to overcome image translation. Experimental results show that it improves the recognition performance and overcomes illumination, translation, noise, and rotation.

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The biometric recognition system based on near-infrared finger vein image

Cited by 27 publications

References 33 publications

FFV-MBC: A Novel Fused Finger-Vein Recognition Method Based on Monogenic Binary Coding

FFV-MBC: A Novel Fused Finger-Vein Recognition Method Based on Monogenic Binary Coding

Feature Recognition and Style Transfer of Painting Image Using Lightweight Deep Learning

Finger Vein Recognition Based on a Histogram of Competitive Gabor Directional Binary Statistics

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