Palm Vein Recognition using Local Tetra Patterns

Saxena, Jai; Teckchandani, Kapish; Pandey, Prithu; Dutta, Malay Kishore; Travieso, Carlos M.; Alonso-Hernandez, Jesus B.

doi:10.1109/iwobi.2015.7160159

Cited by 13 publications

(12 citation statements)

References 10 publications

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“…Kang and Wu [6] proposed a texture-based method, in which a mutual foreground-based linear binary pattern (LBP) was exploited for texture feature extraction. Mirmohamadsadeghi and Drygajlo [41] also proposed a texturebased method, in which two texture descriptors, LBP and local derivative patterns (LDP) were used for palm vein recognition. ManMohan et al [42] proposed a palm vein recognition method using local tetra patterns (LTP).…”

Section: Traditional Palm Vein Recognition Methodsmentioning

confidence: 99%

A Performance Evaluation of Classic Convolutional Neural Networks for 2D and 3D Palmprint and Palm Vein Recognition

Jia

Gao

Xia

et al. 2020

Int. J. Autom. Comput.

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Palmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition, and have achieved impressive results. However, the research on deep learning-based palmprint recognition and palm vein recognition is still very preliminary. In this paper, in order to investigate the problem of deep learning based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct performance evaluation of seventeen representative and classic convolutional neural networks (CNNs) on one 3D palmprint database, five 2D palmprint databases and two palm vein databases. A lot of experiments have been carried out in the conditions of different network structures, different learning rates, and different numbers of network layers. We have also conducted experiments on both separate data mode and mixed data mode. Experimental results show that these classic CNNs can achieve promising recognition results, and the recognition performance of recently proposed CNNs is better. Particularly, among classic CNNs, one of the recently proposed classic CNNs, i.e., EfficientNet achieves the best recognition accuracy. However, the recognition performance of classic CNNs is still slightly worse than that of some traditional recognition methods.

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Section: Traditional Palm Vein Recognition Methodsmentioning

confidence: 99%

A Performance Evaluation of Classic Convolutional Neural Networks for 2D and 3D Palmprint and Palm Vein Recognition

Jia

Gao

Xia

et al. 2020

Int. J. Autom. Comput.

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“…3) Parameter settings for those implemented state-of-the-art methods For Experiment II-(i), the parameters of implemented stateof-art methods are provided in Table 4. Among the compared methods, [16]- [18], [20] adopted multi-scale while [11]- [14], [19] utilized the single scale approach (see Table IV).…”

Section: ) Evaluation Protocolmentioning

confidence: 99%

“…The method in [55] has two parameters namely, K for the number of cells in the image, and N cell for the number of bins in the cell histogram. These parameter values are searched within (K, N cell ) ∈ {(6, 15), (6,18), (12,15), (12,18)} following that in [55]. For RGB palm-vein, the only competing work in the literature by Niklsins [47] (apart from ours) does not use multi-scale score fusion.…”

Section: ) Evaluation Protocolmentioning

confidence: 99%

Palm-Vein Verification Using Images From the Visible Spectrum

Cho

Kim

et al. 2021

IEEE Access

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In this paper, we investigate into utilization of images from the visible light (RGB) spectrum for identity verification based on the palm-veins. This is differentiated from the commonly utilized Nearinfrared (NIR) images for palm-vein feature extraction. Our goal is to explore into the often omitted palm-vein information from the RGB palm images considering the vast deployment of the RGB cameras. Essentially, the vein line features are extracted at various scales based on an efficient difference image projection. The extracted features from the gallery and the probe images are matched based on a modified Hamming distance measure. The resultant similarity scores are finally fused at score level for accuracy enhancement. Experiments are conducted on two public multi-spectral palm databases. The results show encouraging matching accuracy and computational efficiency of the proposed method which extracts the palm-vein utilizing only the visible spectrum. The outcome of this study can be deployed as a standalone biometric or as part of a multibiometric system for secured authentication.

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“…Statistical-based methods: these methods represent the texture of the vein directly from the pixel level of the vein image. Among them are local binary pattern (LBP) [11] [31] [32], local derivative binary pattern [33], local directional texture pattern (LDTP) [34], and Local Tertra Patterns (LTrp) [35]. These statistical techniques depict the vein image in one dimensional histogram but they lack the positional information on the palm vein texture.…”

Section: Introductionmentioning

confidence: 99%

“…These methods attain high recognition results but the main drawback is the high computation time invested in training and validation. There exist several studies in the literature that assigned local descriptors to palm vein feature extraction and representation such as [11] [31]- [35]. To obtain a better representation of the vein pattern using these local descriptors, methods to combine Gabor filter with the local descriptors were suggested for vein recognition (compared with using the descriptor alone) owing to Gabor's high discriminant capability for recognition-based applications as well as its translation, scale and rotation invariance properties.…”

Section: Introductionmentioning

confidence: 99%

Palmvein Recognition Using Block-Based WLD Histogram of Gabor Feature Maps and Deep Neural Network With Bayesian Optimization

et al. 2021

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The demand on devices and systems empowered by biometric verification and identification mechanisms has been increasing in recent years as they have become a significant part of our lives. Palm vein biometric is an emerging technology that has drawn considerable attention from researchers and scientists over the last decade. In the present work, a novel feature extraction methodology named GPWLD combing the Gabor features with positional Weber's local descriptor (PWLD) features is proposed. WLD is a highly representative micro-pattern descriptor that performs well against noise and illumination changes in images. However, it lacks the ability to capture the vein pattern features at different orientations. Moreover, its descriptor packs the local information content into a single histogram that does not take the spatial locality of micro-patterns into consideration. To solve these two issues, firstly, the palm vein image is passed through Gabor filter with different orientations to capture the salient rotational features found in the output feature maps. Secondly, the spatiality is achieved by uniformly dividing each feature map into several blocks. Next, Weber's law feature descriptor (WLD) histogram is computed for each block in every feature map. Finally, these histograms are concatenated to compose the final feature vector. Due to the high dimensionality of the final feature vector, Principal component analysis (PCA) algorithm is utilized for feature size reduction. In the classification stage, a deep neural network (DNN) comprising an optimized stacked autoencoder (SAE) with Bayesian optimization and a softmax layer is used. Optimization of the SAE is carried out by using Bayesian optimization to find the optimal SAE structure and the options of the training algorithm. The Experimental results verify the discriminative power of the extracted features and the accuracy of the proposed DNN. For both Identification and verification experiments, the proposed method attains higher identification rate and lower EER than state-of-the-art methods.INDEX TERMS WLD descriptor, Gabor filter, deep neural network, stacked autoencoder, Bayesian optimization.

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Palm Vein Recognition using Local Tetra Patterns

Cited by 13 publications

References 10 publications

A Performance Evaluation of Classic Convolutional Neural Networks for 2D and 3D Palmprint and Palm Vein Recognition

A Performance Evaluation of Classic Convolutional Neural Networks for 2D and 3D Palmprint and Palm Vein Recognition

Palm-Vein Verification Using Images From the Visible Spectrum

Palmvein Recognition Using Block-Based WLD Histogram of Gabor Feature Maps and Deep Neural Network With Bayesian Optimization

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