Spatial Pyramid Pooling of Selective Convolutional Features for Vein Recognition

Wang, Jun; Pan, Zaiyu; Wang, Guoqing; Li, Ming; Li, Yulian

doi:10.1109/access.2018.2839720

Cited by 29 publications

(12 citation statements)

References 48 publications

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“…A convolution between an indicator filter in a higher layer with a to-be-reweighted filter in a lower layer and a redundancy-driven indicator filter selection algorithm is designed. Wang et al [23] adopted convolutional activations as the regional representation of palm vein images. To obtain more discriminative and robust feature representation, they proposed a spatial weighting convolutional feature model.…”

Section: B Deep Learning Methodsmentioning

confidence: 99%

“…We use accuracy (Acc) and equal error rate (EER) as the evaluation metrics, which are widely used in biometrics [24], [23]. Accuracy is the ratio of the number of correct predictions to the number of test samples.…”

Section: Ser Modelmentioning

confidence: 99%

“…The intra-class matching number is 1,500 (100 × C 1 6 × C 1 5 /2), and the inter-class matching number is 178,200 (100 × 99 × C 1 6 × C 1 6 /2 ). We calculate FAR and FRR as [55], [54], [24], [23], and we draw the ROC curve. We draw the ROC curves for the other comparison methods, as shown in Fig.…”

mentioning

confidence: 99%

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Outside Box and Contactless Palm Vein Recognition Based on a Wavelet Denoising ResNet

Wang

et al. 2021

IEEE Access

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Palm vein recognition is a high-security biometric. Outside the NIR capture box and contactless palm vein recognition are more popular but challenging. The users feel comfortable outside the NIR capture box but face more optical blurring brought by visible light. Contactless capture gestures solve the hygienic problem but face the image rotation, position translation, and scale transformation which makes classification difficult especially in large-scale databases. To address these problems, we develop a wavelet denoising ResNet, which consists of two models: the wavelet denoising (WD) model and the squeeze-and-excitation ResNet18 (SER) model. The WD model focuses on removing noise from skin scattering and optical blurring from palm vein images. The WD model enhances the low-frequency feature into a deep learning feature by residual learning technology. This strategy increases the weight of an effective handcrafted feature in the deep learning network. The SER model overcomes rotation, position translation, and scale transformation by selectively emphasizing classification features and weakening less useful features. To train and verify the network, an inside box palm vein image database and an outside box palm vein image database are set up. The Tongji contactless palm vein image database was also employed in the experiments. The validity and superiority of our network are verified in a series of experiments.

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Section: B Deep Learning Methodsmentioning

confidence: 99%

Section: Ser Modelmentioning

confidence: 99%

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Outside Box and Contactless Palm Vein Recognition Based on a Wavelet Denoising ResNet

Wang

et al. 2021

IEEE Access

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“…Besides, some existing DL models, such as VGGNet [ 38 , 40 , 41 , 42 ], ResNet [ 43 ], and AlexNet [ 44 ], etc., were also introduced. In these models, either a different image or an image pair was fed into the networks.…”

Section: Introductionmentioning

confidence: 99%

A Novel Finger Vein Recognition Method Based on Aggregation of Radon-Like Features

Yao

Song

et al. 2021

Sensors

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Finger vein (FV) biometrics is one of the most promising individual recognition traits, which has the capabilities of uniqueness, anti-forgery, and bio-assay, etc. However, due to the restricts of imaging environments, the acquired FV images are easily degraded to low-contrast, blur, as well as serious noise disturbance. Therefore, how to extract more efficient and robust features from these low-quality FV images, remains to be addressed. In this paper, a novel feature extraction method of FV images is presented, which combines curvature and radon-like features (RLF). First, an enhanced vein pattern image is obtained by calculating the mean curvature of each pixel in the original FV image. Then, a specific implementation of RLF is developed and performed on the previously obtained vein pattern image, which can effectively aggregate the dispersed spatial information around the vein structures, thus highlight vein patterns and suppress spurious non-boundary responses and noises. Finally, a smoother vein structure image is obtained for subsequent matching and verification. Compared with the existing curvature-based recognition methods, the proposed method can not only preserve the inherent vein patterns, but also eliminate most of the pseudo vein information, so as to restore more smoothing and genuine vein structure information. In order to assess the performance of our proposed RLF-based method, we conducted comprehensive experiments on three public FV databases and a self-built FV database (which contains 37,080 samples that derived from 1030 individuals). The experimental results denoted that RLF-based feature extraction method can obtain more complete and continuous vein patterns, as well as better recognition accuracy.

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“…In this framework, the deep activation features are extracted directly from a CNN model pre-trained on ImageNet. Wang et al [15] proposed to adopt a CNN model pre-trained on ImageNet as a universal feature descriptor. This approach represents a hand vein image by concatenating the local features from different levels of the spatial pyramid.…”

Section: Introductionmentioning

confidence: 99%

Exploring Competitive Features Using Deep Convolutional Neural Network for Finger Vein Recognition

Xie

2019

IEEE Access

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Deep convolutional neural networks (DCNN) have been applied successfully for finger vein recognition and have achieved promising performance in the past three years. However, public finger vein datasets are scarce and tend to be relatively small, and thus unable to provide a substantial number of welllabeled images needed to train an effective convolutional neural network (CNN). In this paper, a CNN model pre-trained on ImageNet is adopted to develop a CNN-based local descriptor named CNN competitive order (CNN-CO) that can exploit discriminative features for finger vein recognition. The CNN filters from the first layer of the AlexNet network and the corresponding CNN filtered images are visualized and compared with Gabor filters. According to the appearances and outputs of the CNN filters in three color spaces, we select the ones that most resemble the Gabor filters. The selected CNN filters are employed to generate a competitive order (CO) image using the winner-take-all rule. Then, the pyramidal histograms calculated from the CO image in different levels are concatenated to build the final histogram. The extensive experimental results on two public finger vein datasets demonstrate the effectiveness of the proposed method in selecting CNN filters. The results show that the proposed CNN-CO scheme using the selected CNN filters outperforms the well-known local descriptors. INDEX TERMS Competitive pattern, finger vein, convolutional neural network.

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Spatial Pyramid Pooling of Selective Convolutional Features for Vein Recognition

Cited by 29 publications

References 48 publications

Outside Box and Contactless Palm Vein Recognition Based on a Wavelet Denoising ResNet

Outside Box and Contactless Palm Vein Recognition Based on a Wavelet Denoising ResNet

A Novel Finger Vein Recognition Method Based on Aggregation of Radon-Like Features

Exploring Competitive Features Using Deep Convolutional Neural Network for Finger Vein Recognition

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