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Multiscale Local Phase Quantization for Robust Component-Based Face Recognition Using Kernel Fusion of Multiple Descriptors

Overview of attention for article published in IEEE Transactions on Software Engineering, March 2013
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (63rd percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
1 X user
patent
2 patents

Readers on

mendeley
64 Mendeley
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Article details
Title
Multiscale Local Phase Quantization for Robust Component-Based Face Recognition Using Kernel Fusion of Multiple Descriptors
Published in
IEEE Transactions on Software Engineering, March 2013
DOI 10.1109/tpami.2012.199
Pubmed ID
Authors

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Timeline Attention over time Attention Score history
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X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Geographical breakdown
Country Count As %
United States 1 2%
Taiwan 1 2%
Korea, Republic of 1 2%
Japan 1 2%
United Kingdom 1 2%
Unknown 59 92%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 17 27%
Student > Master 9 14%
Researcher 9 14%
Student > Bachelor 8 13%
Student > Doctoral Student 4 6%
Other 10 16%
Unknown 7 11%
Readers by discipline
Readers by discipline Count As %
Computer Science 25 39%
Engineering 18 28%
Agricultural and Biological Sciences 2 3%
Mathematics 1 2%
Sports and Recreations 1 2%
Other 2 3%
Unknown 15 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 June 2023.
All research outputs
#11,459,380
of 34,364,397 outputs
Outputs from IEEE Transactions on Software Engineering
#3,367
of 7,909 outputs
Outputs of similar age
#85,671
of 245,626 outputs
Outputs of similar age from IEEE Transactions on Software Engineering
#37
of 74 outputs
Altmetric has tracked 34,364,397 research outputs across all sources so far. This one has received more attention than most of these and is in the 65th percentile.
So far Altmetric has tracked 7,909 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.0. This one has gotten more attention than average, scoring higher than 55% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 245,626 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.
We're also able to compare this research output to 74 others from the same source and published within six weeks on either side of this one. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.