A palmprint ROI extraction method for mobile devices in complex environment

Chai, Tingting; Wang, Shenghui; Sun, Dongmei

doi:10.1109/icsp.2016.7878045

Cited by 18 publications

(15 citation statements)

References 17 publications

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“…Their target is enhancing the contrast of the palm region and the background. For example, in [28], the mobile phone's built-in LED flash is utilized for palm segmentation. When the flash is turned on, the palm surface is much brighter than the background, because the palm is much closer to the camera than the background.…”

Section: Classical Methodsmentioning

confidence: 99%

“…4. Convex hull-based methods [28,42,43]. The minimum polygon is detected to encapsulate the palm contour.…”

Section: Classical Methodsmentioning

confidence: 99%

“…Then, what we need to do is scale normalization, which means to determine the side length of the ROI. The work reported in [10,17,21,30,33,47] utilized the palm width to determine the size of the ROI, while the work reported in [4,[27][28][29] utilized the length of the tangent line to determine the size of the ROI. In [30], the author found that big ROI performs better.…”

Section: Classical Methodsmentioning

confidence: 99%

“…Its main idea is first detecting the keypoints of the finger valleys and then establishing a local coordinate system based on the detected keypoints, so that the ROI coordinates are determined based on the palm direction and position (as is shown in Figure 2). Most of the current ROI localization methods [10,[27][28][29][30][31][32] are based on this strategy.…”

Section: Related Work On Roi Localizationmentioning

confidence: 99%

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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: Classical Methodsmentioning

confidence: 99%

“…4. Convex hull-based methods [28,42,43]. The minimum polygon is detected to encapsulate the palm contour.…”

Section: Classical Methodsmentioning

confidence: 99%

Section: Classical Methodsmentioning

confidence: 99%

Section: Related Work On Roi Localizationmentioning

confidence: 99%

See 2 more Smart Citations

Region of Interest Localization Methods for Publicly Available Palmprint Databases

Xu¹,

Fan²,

Li³

et al. 2021

Biometric Systems

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show abstract

“…For palmprint recognition, region of interest (ROI) extraction is a vital and necessary step, where the resulting location of the ROI will make a direct impact to feature extraction and recognition [19]. In ROI extraction we automatically and reliably segment a small region from the captured palmprint containing a wealth of information [20]. This reduces the amount of data in feature extraction and matching as well as any influences caused by the rotation and translation of the palm [8].…”

Section: Introductionmentioning

confidence: 99%

Robust and adaptive algorithm for hyperspectral palmprint region of interest extraction

Zhao

Zhang

2019

IET biom.

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Recently, hyperspectral imaging has attracted more and more considerable research attention because of its discriminative information. This study proposes a robust approach to adaptively extract the hyperspectral palmprint region of interest (ROI) captured by a hyperspectral palmprint acquisition device, which is considered one of the most important stages in palmprint recognition. For different spectral wavelengths, the image has different illuminations and unbalanced shadows. In particular, mean grey values of palm images in different bands have large variations, such that binarisation of the palm image can be considered a challenging task to accurately separate the contour of the palm from the original image. To solve these problems, this study proposes an adaptive ROI segmentation algorithm, whereby a support vector machine-based method is used to detect the palm from the image and a coordinate established to ensure the accuracy of the ROI. The proposed method has been tested on a hyperspectral palm data set which covers spectrums from 530-1030 nm with 20 nm intervals. The experimental results showed that the proposed algorithm is effective and efficient at locating the ROI in hyperspectral palmprint images, where local binary pattern features were extracted from the ROIs achieving an equal error rate (EER) of 1.49% and an accuracy of 99.51% in recognition.

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