מידע ביבליוגרפי
| כותר: |
ResNet18 facial feature extraction algorithm improved based on hybrid domain attention mechanism. |
| Authors: |
Mei Y; Faculty of Intelligent Transportation, Anhui Sanlian University, Hefei, China. |
| Source: |
PloS one [PLoS One] 2025 Mar 19; Vol. 20 (3), pp. e0319921. Date of Electronic Publication: 2025 Mar 19 (Print Publication: 2025). |
| Publication Type: |
Journal Article |
| שפה: |
English |
| Journal Info: |
Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Imprint Name(s): |
Original Publication: San Francisco, CA : Public Library of Science |
| MeSH Terms: |
Algorithms* , Face*/anatomy & histology , Automated Facial Recognition*/methods , Image Processing, Computer-Assisted*/methods, Humans ; Facial Recognition |
| Abstract: |
Competing Interests: The authors have declared that no competing interests exist.
In the research of face recognition technology, the traditional methods usually show poor recognition accuracy and insufficient generalization ability when faced with complex scenes such as lighting changes, posture changes and skin color diversity. To solve these problems, based on the improvement of adaptive boosting to improve the accuracy of face detection, the study proposes a residual network 18-layer face feature extraction algorithm based on hybrid domain attention mechanism algorithm. The study introduces channel-domain and spatial-domain attention mechanism to enhance the extraction of face image features. The outcomes indicated that the recognition accuracy of the proposed method on multiple face image datasets, labeled field face datasets, and celebrity facial attribute datasets exceeded 98.34% and reached up to 99.64%, which was better than the current state-of-the-art methods. After combining channel and spatial attention mechanism, the false detection rate was as low as 2.50%, which was lower than the false detection rate of other methods. In addition to enhancing face recognition's robustness and accuracy, the work offers fresh concepts and resources for face recognition's potential uses in intricate scenarios in the future.
(Copyright: © 2025 Yingying Mei. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.) |
| References: |
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| Entry Date(s): |
Date Created: 20250319 Date Completed: 20250513 Latest Revision: 20250513 |
| Update Code: |
20250514 |
| PubMed Central ID: |
PMC11922290 |
| DOI: |
10.1371/journal.pone.0319921 |
| PMID: |
40106476 |
| Database: |
MEDLINE |
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