Palm vein recognition based on a modified $$\text{(2D)}^{2}\text{ LDA}$$

Lee, Yen-Po

doi:10.1007/s11760-013-0425-6

Cited by 31 publications

(19 citation statements)

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“…For subspace learning-based methods, the LDA [45] , 2DLDA [46] , (2D) 2 LDA [47] , and sparse representation methods [48] have been studied for palm vein recognition. Fig.…”

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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“…For subspace learning-based methods, the LDA [45] , 2DLDA [46] , (2D) 2 LDA [47] , and sparse representation methods [48] have been studied for palm vein recognition. Fig.…”

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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“…Yang and Sun [82] extracted palm vein and palmprint feature with a LBP and match them with canonical correlation analysis (CCA). CCA was a standard tool in statistical analysis that DCT and k-PCA Euclidean distance CASIA V1.0 (left hands) 100 6 CRR = 99.989% [74] phase symmetric learning vector quantisation Self-built 47 10 CRR = 94% [65] two-dimensional linear discriminant cosine similarity CASIA V1.0 (left hands) 100 6 EER = 8% [4] modified (2D) 2 LDA minimum distance classifier Self-built 207 20 CRR = 99.41% [62] PCA PNN CASIA V1.0 200 6 CRR = 84% measures the linear relationship between two data sets. Trabelsi [83] fused palm vein and palmprint in the feature level.…”

Section: Palm Vein Fusion Recognitionmentioning

confidence: 99%

“…Jen‐Chun Lee [24] and Yen‐Po Lee [4] also used a CCD camera – an adapted Sony XC711, costing about the U.S. $1500. The palm vein product of Fujitsu costs $5000 for the whole system [16].…”

Section: Palm Vein Image Acquisitionmentioning

confidence: 99%

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Review of palm vein recognition

Elliott

Lin

et al. 2019

IET biom.

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As a new research focus in biometrics, palm vein recognition has attracted people's attention because of high security, liveness-detection, user acceptability and convenience. Palm vein recognition exhibits high security, as it uses the network of blood vessels underneath palm skin for recognition. As palm vein is interior biological information of the body, vein patterns are much harder for intruders to copy compared to other biometric features. Palm vein is mostly invisible to the human eyes; they are commonly captured under near-infrared light. Under its natural state, the veins of the palm are concealed naturally and, for most of time people's hands are in the gesture of half fist. This study presents a comprehensive overview of recent research progress of palm vein recognition, from the basic background knowledge to data acquisition, public database, preprocessing, feature extraction and matching. In addition, the study focuses on palm vein-related fusion. Ultimately, the authors discuss the challenges and future perspectives in palm vein recognition for further works.

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“…Existing literature on palmvein recognition mainly focuses on recognition algorithms, such as ROI extraction, image enhancement, feature extraction, and matching [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]41]. A few studies have focused on palmvein image capture device to improve the quality of palmvein image; the literature can be summarized into three research contents, namely, obtaining a palmvein image, designing the structure of the palmvein image-capture device, and choosing the near-infrared light source and image sensor [31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Image and Graphics

2015

Lecture Notes in Computer Science

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Abstract. Scene text recognition is gaining renewed interest owing to the increase of scene image based applications and new intelligent devices. Unlike recognition of printed text, scene text recognition is challenging due to the complexity of scene images. To provide an overview of the techniques and inspire future research, this paper reviews the advances in scene character and text recognition, with emphasize on character feature representation methods and word recognition models. The papers published in the most recent conferences ECCV 2014, ACCV 2014, ICIP 2014, and ICPR 2014 are also reviewed in this paper to provide the state-of-the-art of scene character and text recognition. The state-of-the-art performance is provided to show the achieved performance so far and demonstrate the potential of deep learning based methods.

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Palm vein recognition based on a modified $$\text{(2D)}^{2}\text{ LDA}$$

Cited by 31 publications

References 20 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

Review of palm vein recognition

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