Zobrazeno 1 - 10
of 1 123
pro vyhledávání: '"finger vein recognition"'
Publikováno v:
EURASIP Journal on Image and Video Processing, Vol 2024, Iss 1, Pp 1-28 (2024)
Abstract Finger vein recognition is an emerging biometric trait known for its privacy features. Despite the remarkable performance of deep learning methods like convolutional neural networks on challenging finger vein datasets, their reliability and
Externí odkaz:
https://doaj.org/article/f7e8fb2076b64e978339fd01b70d61c1
Publikováno v:
IEEE Access, Vol 12, Pp 76909-76918 (2024)
Deep learning-based finger vein image recognition methods usually suffer from high complexity, insufficient global information extraction, and overfitting. The use of lightweight networks can significantly reduce the accuracy owing to the reduction i
Externí odkaz:
https://doaj.org/article/a8883dd96170443896424edf4cc76cee
Publikováno v:
IEEE Access, Vol 12, Pp 1943-1951 (2024)
To solve the problem of low accuracy and high computational resource consumption in finger vein recognition, a finger vein recognition model based on ResNet with self-attention (FV-RSA) is proposed. This model combines global focusing ability of self
Externí odkaz:
https://doaj.org/article/7b847b593dec4000804eb3b8546b59ac
Autor:
LI Jie, QU Zhong
Publikováno v:
Jisuanji kexue yu tansuo, Vol 17, Iss 11, Pp 2557-2579 (2023)
Finger vein recognition technology has become a research hotspot in the new generation of biometrics because of its advantages of non-contact, high security and living body detection. With the development of deep learning, finger vein recognition tec
Externí odkaz:
https://doaj.org/article/250dcc09802b4577ad4a0ca661ef1df2
Publikováno v:
Sensors, Vol 24, Iss 15, p 4779 (2024)
Finger vein recognition methods, as emerging biometric technologies, have attracted increasing attention in identity verification due to their high accuracy and live detection capabilities. However, as privacy protection awareness increases, traditio
Externí odkaz:
https://doaj.org/article/5140b86fa8294178a8f4fd632eafd552
Autor:
Xiao Ma, Xuemei Luo
Publikováno v:
Mathematical Biosciences and Engineering, Vol 20, Iss 6, Pp 11081-11100 (2023)
Deep learning is an important technology in the field of image recognition. Finger vein recognition based on deep learning is one of the research hotspots in the field of image recognition and has attracted a lot of attention. Among them, CNN is the
Externí odkaz:
https://doaj.org/article/11a10e47c14f44f18d1cbb86aba32f30
Autor:
Xiaoye Li, Bin-Bin Zhang
Publikováno v:
IEEE Access, Vol 11, Pp 75451-75461 (2023)
Vision Transformer (ViT) has drawn the attention of many researchers in computer vision due to its superior performance in many computer vision tasks. However, there is limited research based on ViT models in finger vein recognition. This may be beca
Externí odkaz:
https://doaj.org/article/89b494afe3f8499c8feadf03e490a102
Publikováno v:
Journal of King Saud University: Computer and Information Sciences, Vol 34, Iss 10, Pp 9343-9362 (2022)
Finger-vein recognition is a biometric technology, which makes use of the pattern of veins in the skin of fingers. Finger-vein recognition is widely employed owing to its high recognition rate and ease of use. A spoof detection method is essential fo
Externí odkaz:
https://doaj.org/article/59c9096bb2df463e9ac579cc2b3928cf
Publikováno v:
Sensors, Vol 24, Iss 4, p 1331 (2024)
In addressing challenges related to high parameter counts and limited training samples for finger vein recognition, we present the FV-MViT model. It serves as a lightweight deep learning solution, emphasizing high accuracy, portable design, and low l
Externí odkaz:
https://doaj.org/article/677d564505e34a139926cd601d23c5cf
Publikováno v:
Meikuang Anquan, Vol 53, Iss 4, Pp 167-171 (2022)
In view of the existing well detection methods and devices, the detection effect is not ideal, the detection efficiency is low, and the detection results are not accurate. This paper proposed a unique detection technology scheme based on finger vein
Externí odkaz:
https://doaj.org/article/7540e16a77cb44fea016a2e315bd47ac