• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.3 s.309
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• pp.82-89
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• 2006

Palmprint-based personal identification system, as a new member in the biometrics system family, has become an active research topic in recent years. Although lots of methods have been made, how to represent palmprint for effective classification is still an open problem and conducting researches. In this paper, the palmprint classification and recognition method based on PCA (Principal Components Analysis) using the dimension reduction of singular vector is proposed. And the 135dpi palmprint image which is obtained by the palmprint acquisition device is used for the effectual palmprint recognition system. The proposed system is consists of the palmprint acquisition device, DB generation algorithm and the palmprint recognition algorithm. The palmprint recognition step is limited 2 times. As a results GAR and FAR are 98.5% and 0.036%.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.2 s.302
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• pp.31-38
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• 2005

Recently, Biometrics-based personal identification is regarded as an effective method of person's identity with recognition automation and high performance. In this paper, the palmprint recognition method based on Hu invariant moment is proposed. And the low-resolution(750dpi) palmprint image$(5.5Cm\times5.5Cm)$ is used for the small scale database of the effectual palmprint recognition system. The proposed system is consists of two parts: firstly, the palmprint fixed equipment for the acquisition of the correctly palmprint image and secondly, the algorithm of the efficient processing for the palmprint recognition. And the palmprint identification step is limited 3 times. As a results, when the coefficient is 0.001 then FAR and GAR are $0.038\%$ and $98.1\%$ each other. The authors confirmed that FAR is improved $0.002\%$ and GAR is $0.1\%$ each other compared with [3].

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1439-1447
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• 2018

Palm image acquisition without contact has advantages in user convenience and hygienic issues, but such images generally display more image variations than those acquired employing a contact plate or pegs. Therefore, it is necessary to develop a palmprint identification method which is robust to affine variations. This study proposes a deep learning approach which can effectively identify contactless palmprints. In general, it is very difficult to collect enough volume of palmprint images for training a deep convolutional neural network(DCNN). So we adopted an approach to use a pretrained DCNN. We designed two new DCNNs based on the VGGNet. One combines the VGGNet with SVM. The other add a shallow network on the middle-level of the VGGNet. The experimental results with two public palmprint databases show that the proposed method performs well not only contact-based palmprints but also contactless palmprints.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.11
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• pp.495-502
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• 2014

An effective solution to the variation on scale and rotation is required to recognize contactless palmprint. In this study, we firstly minimize the variation by extracting a region of interest(ROI) according to the size and orientation of hand and normalizing the ROI. This paper proposes a contactless palmprint recognition method based on KLT(Kanade-Lukas-Tomasi) feature points. To detect corresponding feature points, texture in local regions around KLT feature points are compared. Then, we recognize palmprint by measuring the similarity among displacement vectors which represent the size and direction of displacement of each pair of corresponding feature points. An experimental results using CASIA public database show that the proposed method is effective in contactless palmprint recognition. Especially, we can get the performance of exceeding 99% correct identification rate using multiple Gabor filters.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.757-770
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• 2022

Palmprint recognition has drawn increasingly attentions in the past decade due to its uniqueness and reliability. Traditional palmprint recognition methods usually use high-resolution images as the identification basis so that they can achieve relatively high precision. However, high-resolution images mean more computation cost in the recognition process, which usually cannot be guaranteed in mobile computing. Therefore, this paper proposes an improved low-resolution palmprint image recognition method based on residual networks. The main contributions include: 1) We introduce a channel attention mechanism to refactor the extracted feature maps, which can pay more attention to the informative feature maps and suppress the useless ones. 2) The ResStage group structure proposed by us divides the original residual block into three stages, and we stabilize the signal characteristics before each stage by means of BN normalization operation to enhance the feature channel. Comparison experiments are conducted on a public dataset provided by the Hong Kong Polytechnic University. Experimental results show that the proposed method achieve a rank-1 accuracy of 98.17% when tested on low-resolution images with the size of 12dpi, which outperforms all the compared methods obviously.