Dorsal Hand Vein Image Enhancement for Improve Recognition Rate Based on SIFT Keypoint Matching

Chanthamongkol, Sathaporn; Purahong, Boonchana; Lasakul, Attasit

doi:10.2991/3ca-13.2013.44

Cited by 6 publications

(4 citation statements)

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“…To reduce the computation time and improve the calculation speed, the hand vein images are always low quality, so most of methods are preceded by a preprocessing step which can make it easier to extract vein features. Redhouane applied a double adaptive histogram equalization to enhance the contrast of the images [4]; Chanthamongkol used the contrast-limited adaptive histogram equalization in the enhancement of dorsal vein [5]; Guo Dan et al used multi-scale vessel enhancement filtering to enhance the noise-depressed images [6]; Trabelsi used contrast limited adaptive histogram equalization, moreover histogram equalization and adaptive histogram equalization algorithm are used to contrast with [7,8]; Maurício Ramalho used median and wiener filter [9]; Ramsoful used median and Gaussian filter [10]; Wei used Gaussian and high pass filter [11]; Kumar used Mexican Hat to enhance image. With a view to the importance of pre-processing, the guided filter [12] is proposed to enhance the hand vein images.…”

Section: Introductionmentioning

confidence: 99%

Dorsal Hand Vein Enhancement and Fake Vein Detection

Jiang¹,

Liu²,

Kang³

et al. 2017

dtcse

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Abstract. In order to extract better vein feathers, image preprocessing is necessary. So this paper propose a new approach to enhance images. The enhancement algorithm uses guided filter (GF) to process hand vein images. The guided filter is used as an edge-preserving smoothing operator. The guided filter enhancement algorithm is effective comparing with bilateral filter (BF), histogram equalization (HE), adaptive histogram equalization algorithm (AHE) and contrast limited adaptive histogram equalization (CLAHE). We use several methods to enhance dorsal hand vein images, the recognition rate with guided filter is the best. For the security, a fake vein detection algorithm is used to discriminate the real vein and fake vein images.

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Section: Introductionmentioning

confidence: 99%

Dorsal Hand Vein Enhancement and Fake Vein Detection

Jiang¹,

Liu²,

Kang³

et al. 2017

dtcse

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show abstract

“…These techniques include manual cropping with a combined matched filter and a local binary fitting model to locate tiny boundaries (small veins) in images [15]. The image centroid technique is also used for the segmentation of DHVs [16]. The major drawbacks of these manual segmentation techniques are the loss of significant information, they are time and labor-consuming, and there is high variability in the produced results because the process is based on judgments made using subjective intuition.…”

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confidence: 99%

“…Histogram equalization (HE) is the most widely used traditional enhancing method to improve the intensity of an image globally rather than in the area of interest [21]. Among the variations in HE, contrastlimited adaptive histogram equalization (CLAHE) has been found to be an effective method to enhance the targeted area of DHV images [16,17,22].…”

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confidence: 99%

“…Other research [24] has used convolutional neural network (CNN)-based model for segmentation. Unlike conventional ML methods, which can be timeconsuming for manually determining the features for DHV recognition [16], CNN is an increasingly popular tool for decision-making. A CNN model was first introduced by Le-Cun et al [25] to make recognition training simple and timeefficient (in the automatic extraction of useful information).…”

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Dorsal Hand Vein Pattern Recognition: A Comparison between Manual and Automatic Segmentation Methods

et al. 2023

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Objectives: Various techniques for dorsal hand vein (DHV) pattern extraction have been introduced using small datasets with poor and inconsistent segmentation. This work compared manual segmentation with our proposed hybrid automatic segmentation method (HHM) for this classification problem.Methods: Manual segmentation involved selecting a region-of-interest (ROI) in images from the Bosphorus dataset to generate ground truth data. The HHM combined histogram equalization and morphological and thresholding-based algorithms to localize veins from hand images. The data were divided into training, validation, and testing sets with an 8:1:1 ratio before training AlexNet. We considered three image augmentation strategies to enlarge our training sets. The best training hyperparameters were found using the manually segmented dataset.Results: We obtained a good test accuracy (91.5%) using the model trained with manually segmented images. The HHM method showed slightly inferior performance (76.5%). Considerable improvement was observed in the test accuracy of the model trained with the inclusion of automatically segmented and augmented images (84%), with low false acceptance and false rejection rates (0.00035% and 0.095%, respectively). A comparison with past studies further demonstrated the competitiveness of our technique.Conclusions: Our technique can be feasible for extracting the ROI in DHV images. This strategy provides higher consistency and greater efficiency than the manual approach.

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Logarithmic transformation for enhancing keypoint matching of SIFT in augmented reality

Pambudi¹,

Fauzan²,

B.³

et al. 2022

AIP Conference Proceedings

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Dorsal Hand Vein Image Enhancement for Improve Recognition Rate Based on SIFT Keypoint Matching

Abstract: Abstract-The quality of im automatic sys high recognize in this paper, by CLAHE(C that improve Gaussian and composed by for each image matching step the performa result shows t Gaussian and time matching

Cited by 6 publications

References 0 publications

Dorsal Hand Vein Enhancement and Fake Vein Detection

Dorsal Hand Vein Enhancement and Fake Vein Detection

Dorsal Hand Vein Pattern Recognition: A Comparison between Manual and Automatic Segmentation Methods

Logarithmic transformation for enhancing keypoint matching of SIFT in augmented reality

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