Combining multi‐wavelet and CNN for palmprint recognition against noise and misalignment

Zhou, Lijian; Guo, Han; Lin, Shanshan; Hao, Siyuan; Zhao, Kun

doi:10.1049/iet-ipr.2018.6122

Cited by 10 publications

(2 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To sum up, it can be seen that the current volleyball video analysis system and the traditional image recognition model generally have the problems of poor recognition effect, low recognition accuracy, poor stability, and low data utilization in volleyball video analysis [ 18 , 19 ]. On the other hand, in the existing volleyball video analysis system, the vast majority of volleyball video intelligent recognition methods can only recognize a single volleyball video information and can not distinguish volleyball sports with obvious differences, so they do not have intelligent characteristics [ 20 – 22 ].…”

Section: State Of the Artmentioning

confidence: 99%

Analysis of Volleyball Video Intelligent Description Technology Based on Computer Memory Network and Attention Mechanism

Zhang

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

There are some problems in the process of video intelligent description and analysis of volleyball, such as poor effective information extraction rate and poor dynamic tracking effect. Based on this, combined with long-term and short-term memory network and attention mechanism, this paper designs an intelligent description model of volleyball video based on deep learning algorithm and studies how to improve the extraction rate of volleyball video information through intelligent detection hardware and image recognition technology. This paper first introduces the application of image recognition technology and deep learning algorithm in the intelligent description of volleyball video, then designs the volleyball video and image recognition model based on deep learning algorithm according to the requirements of volleyball video intelligent description, and selects three correlation factors related to the impact indicators of volleyball skills. This study selects three characteristic parameters associated with volleyball video analysis indexes, namely, take-off, bounce, and hand movement, combined with image sensing hardware assisted sensor network to realize real-time monitoring of action state in volleyball video analysis system. The experimental results show that, compared with the current mainstream sports video intelligent analysis and image recognition methods with data analysis as the core, the intelligent volleyball sports video intelligent description and image recognition system based on the integration of deep learning algorithm and sensor hardware assistance has the advantages of good detection effect, high data effectiveness, low cost, and high efficiency of volleyball sports video analysis. It can effectively improve the efficiency of volleyball video intelligent description.

show abstract

Section: State Of the Artmentioning

confidence: 99%

Analysis of Volleyball Video Intelligent Description Technology Based on Computer Memory Network and Attention Mechanism

Zhang

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Multi-wavelets simultaneously capture symmetry, orthogonality, compact support, and high vanishing moments. The suggested technique demonstrates a remarkable ability to accurately identify palmprints, even when there is interference from noise and misalignment [24]. The Wiener filter eliminates noise artifacts, such as blurring, from iris and palmprint photos, thereby boosting crucial textural regions within the images [25].…”

Section: Introductionmentioning

confidence: 99%

Image enhancement in palmprint recognition: a novel approach for improved biometric authentication

Kusban,

Budiman,

Hari Purwoto

2024

IJECE

View full text Add to dashboard Cite

Several researchers have used image enhancement methods to reduce detection errors and increase verification accuracy in palmprint identification. Divergent opinions exist among experts regarding the best method of image filtering to improve image palmprint recognition. Because of the unique characteristics of palmprints and the difficulties in preventing counterfeiting, image-filtering techniques are the subject of this current research. Researchers hope to create the best biometric system possible by utilizing various techniques. These techniques include image enhancement, Gabor orientation scales, dimension reduction techniques, and appropriate matching strategies. This study investigates how different filtering approaches might be combined to improve images. The palmprint identification system uses a 3W filter, which combines wavelet, Wiener, and weighted filters. Optimizing results entails coordinating the 3W filter with Gabor orientation scales, matching processes, and dimension reduction methods. The research shows that accuracy may be considerably increased using a 3W filter with a Gabor orientation scale of [8×7], the kernel principal component analysis (KPCA) dimension reduction methodology, and a cosine matching method. Specifically, a value of 99.722% can be achieved. These results highlight the importance of selecting appropriate settings and techniques for palmprint recognition systems.

show abstract

Palmprint Recognition Using SC-LNMF Model in Gabor Domain

Shang,

Huang

2024

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Combining multi‐wavelet and CNN for palmprint recognition against noise and misalignment

Cited by 10 publications

References 35 publications

Analysis of Volleyball Video Intelligent Description Technology Based on Computer Memory Network and Attention Mechanism

Analysis of Volleyball Video Intelligent Description Technology Based on Computer Memory Network and Attention Mechanism

Image enhancement in palmprint recognition: a novel approach for improved biometric authentication

Palmprint Recognition Using SC-LNMF Model in Gabor Domain

Contact Info

Product

Resources

About