Palmprint Recognition in Uncontrolled and Uncooperative Environment

Matkowski, Wojciech Michal; Chai, Tingting; Kong, Adams Wai-Kin

doi:10.1109/tifs.2019.2945183

Cited by 104 publications

(74 citation statements)

References 57 publications

(139 reference statements)

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“…However, it is not sufficient and it leads the algorithms being sensitive to palm postures and background objects. In recent years, many new methods have been proposed, such as the active shape model (ASM)-based methods [48,49], the active appearance model (AAM)-based methods [17,29,50], the regression tree-based methods [47], and the deep learning-based methods [24,41]. The new-generation methods utilized both the edge and texture information to learn much more robust models to regress the landmarks.…”

Section: New-generation Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Region of Interest Localization Methods for Publicly Available Palmprint Databases

Xu¹,

Fan²,

Li³

et al. 2021

Biometric Systems

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So far, there exist many publicly available palmprint databases. However, not all of them have provided the corresponding region of interest (ROI) images. If everyone uses their own extracted ROI images for performance testing, the final accuracy is not strictly comparable. Since ROI localization is the critical stage of palmprint recognition. The location precision has a significant impact on the final recognition accuracy, especially in unconstrained scenarios. This problem has limited the applications of palmprint recognition. However, many currently published surveys only focus on feature extraction and classification methods. Throughout these years, many new ROI localization methods have been proposed. In this chapter, we will group the existing ROI localization methods into different categories, analyze their basic ideas, reproduce some of the codes, make comparisons of their performances, and provide further directions. We hope this could be a useful reference for further research.

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Section: New-generation Methodsmentioning

confidence: 99%

“…In [24], based on VGG-16 [52], the authors designed an end-to-end neural network to localize the hand landmarks, generate the aligned ROI, and do feature extraction and recognition tasks at the same time. The hand region is extracted from the original Internet image, and then it is resized to 227 Â 227 pixels.…”

Section: New-generation Methodsmentioning

confidence: 99%

Region of Interest Localization Methods for Publicly Available Palmprint Databases

Xu¹,

Fan²,

Li³

et al. 2021

Biometric Systems

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show abstract

“…Genovese et al [83] proposed the method of PalmNet, which is a CNN that uses a method to tune palmprint specific filters through an unsupervised procedure based on Gabor responses and principal component analysis (PCA). Zhong and Zhu [84] proposed an end-to-end method for open-set 2D palmprint recognition by applying CNN with a novel loss function, i.e., centralized large margin cosine loss (C-LMCL). In order to solve the problem of palmprint recognition in uncontrolled and uncooperative environments, Matkowski et al [85] proposed end-to-end palmprint recognition network (EE-PRnet) consisting of two main networks, i.e., ROI localization and alignment network (ROI-LAnet) and feature extraction and recognition network (FERnet).…”

Section: Inception_resnet_v2mentioning

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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“…In this paper (21) , the authors state that, basically there are two types of palmprint recognition: First is online palm print identification and second is latent palmprint identification. The first one will happen with a digital camera where the user of palm print will cooperate to take palm print.…”

Section: Related Workmentioning

confidence: 99%

Fast palmprint retrieval using speed up robust features

Kumar¹

2020

IJST

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Background/Objective: Biometric usage is increasing in exponential series in all organisations for multiple purposes like employee attendance, Aadhaar based authentication and secure login using finger print etc. This biometric process should be as quick as possible without making much delay to retrieve the respective finger print. So an efficient quick retrieval procedure is required, in this regards a fast retrieval method for palm prints is proposed in this article. Method: This method uses Speed up Robust Features (SURF) and an efficient look up table for fast retrieval of palm prints. A key is computed for each palmprint by matching with a pre-selected palmprint called representative. This key is used, to place the palmprint into the look up table like traditional database record. To identify a query palmprint, key is computed and selects a set of palm prints from the look up table which are having similar key as possible matches. Findings: This proposed solution is experimented with multiple representative images to check the improved performance. As an outcome we could achieve better hit rate by comparing with existing system. Novelty: This proposed method makes the new palm prints dynamically without disturbing the current records in the system. The entire solution is experimented on benchmark PolyU palmprint database of 7,753 images and significant performance is shown in results. This proposed solution shows better results with respect to hit rate and miss rate.

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Palmprint Recognition in Uncontrolled and Uncooperative Environment

Cited by 104 publications

References 57 publications

Region of Interest Localization Methods for Publicly Available Palmprint Databases

Region of Interest Localization Methods for Publicly Available Palmprint Databases

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

Fast palmprint retrieval using speed up robust features

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