HandNet

Yuan, Yixuan; Tang, Chaoying; Xia, Shuhang; Zhou, Chen; Qi, Tao

doi:10.1145/3448823.3448838

Cited by 8 publications

(2 citation statements)

References 6 publications

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“…The test results achieve accuracy rate of 96%. Yuan et al [19] proposed an approach that is focused on recognizing people based on hand images. First, the image is preprocessed using the CLAHE method, then images are normalized.…”

Section: Related Workmentioning

confidence: 99%

Human Identification Based on SIFT Features of Hand Image

Rajab¹

2023

IJCDS

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The use of hand features for human identification is a reliable and convenient method that has a greater influence than other biometric techniques because of its simplicity and dependability. In this study, we suggested a technique for identifying individuals using all the features of a hand image which differs from the previous methods of identifying individuals depending on either the features of the palmprint only or the features of the fingerprint only. Therefore, the paper aims to build an efficient system to identify individuals depending on the features of the hand image using Scale Invariant Feature Transform (SIFT) features. Our proposed system comprises three main stages. The first stage includes removing the background and unimportant objects and extracting the hand area from the captured image. The SIFT feature is then applied to extract the robust features from the hand area. Finally, the matching stage depends on the maximum matching points of the SIFT points between the test and training image. The outcomes demonstrate the correct recognition rate (CRR), accuracy, and recall are 99.65%, 99.32%, and 98.62%, respectively. Thus, the effectiveness and efficiency of the suggested approach have been proven.

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Section: Related Workmentioning

confidence: 99%

Human Identification Based on SIFT Features of Hand Image

Rajab¹

2023

IJCDS

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“…Full hand images have been used in [5] to identify a person using both traditional and deep learning methods. Similar appraoch has also been used in [13] with additional data type for fusion, near-infrared (NIR) images, in addition to the RGB hand images. However, these methods are not an end-to-end.…”

Section: Introductionmentioning

confidence: 99%