LCAM: Low-Complexity Attention Module for Lightweight Face Recognition Networks Article Swipe
Seng Chun Hoo
,
Haidi Ibrahim
,
Shahrel Azmin Suandi
,
Theam Foo Ng
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/math11071694
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/math11071694
Inspired by the human visual system to concentrate on the important region of a scene, attention modules recalibrate the weights of either the channel features alone or along with spatial features to prioritize informative regions while suppressing unimportant information. However, the floating-point operations (FLOPs) and parameter counts are considerably high when one is incorporating these modules, especially for those with both channel and spatial attentions in a baseline model. Despite the success of attention modules in general ImageNet classification tasks, emphasis should be given to incorporating these modules in face recognition tasks. Hence, a novel attention mechanism with three parallel branches known as the Low-Complexity Attention Module (LCAM) is proposed. Note that there is only one convolution operation for each branch. Therefore, the LCAM is lightweight, yet it is still able to achieve a better performance. Experiments from face verification tasks indicate that LCAM achieves similar or even better results compared with those of previous modules that incorporate both channel and spatial attentions. Moreover, compared to the baseline model with no attention modules, LCAM achieves performance values of 0.84% on ConvFaceNeXt, 1.15% on MobileFaceNet, and 0.86% on ProxylessFaceNAS with respect to the average accuracy of seven image-based face recognition datasets.
Related Topics
Concepts
FLOPS
Computer science
Face (sociological concept)
Convolution (computer science)
Facial recognition system
Channel (broadcasting)
Point (geometry)
Artificial intelligence
Baseline (sea)
Pattern recognition (psychology)
Image (mathematics)
Machine learning
Computer engineering
Artificial neural network
Parallel computing
Mathematics
Oceanography
Sociology
Social science
Geometry
Geology
Computer network
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/math11071694
- https://www.mdpi.com/2227-7390/11/7/1694/pdf?version=1680493535
- OA Status
- gold
- Cited By
- 1
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4362591604
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4362591604Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/math11071694Digital Object Identifier
- Title
-
LCAM: Low-Complexity Attention Module for Lightweight Face Recognition NetworksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-04-01Full publication date if available
- Authors
-
Seng Chun Hoo, Haidi Ibrahim, Shahrel Azmin Suandi, Theam Foo NgList of authors in order
- Landing page
-
https://doi.org/10.3390/math11071694Publisher landing page
- PDF URL
-
https://www.mdpi.com/2227-7390/11/7/1694/pdf?version=1680493535Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2227-7390/11/7/1694/pdf?version=1680493535Direct OA link when available
- Concepts
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FLOPS, Computer science, Face (sociological concept), Convolution (computer science), Facial recognition system, Channel (broadcasting), Point (geometry), Artificial intelligence, Baseline (sea), Pattern recognition (psychology), Image (mathematics), Machine learning, Computer engineering, Artificial neural network, Parallel computing, Mathematics, Oceanography, Sociology, Social science, Geometry, Geology, Computer networkTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1Per-year citation counts (last 5 years)
- References (count)
-
48Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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