Comparison of face recognition technologies.
<div><p>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...
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2025
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| _version_ | 1852021995920687104 |
|---|---|
| author | Yingying Mei (13817440) |
| author_facet | Yingying Mei (13817440) |
| author_role | author |
| dc.creator.none.fl_str_mv | Yingying Mei (13817440) |
| dc.date.none.fl_str_mv | 2025-03-19T17:33:57Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0319921.g009 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Comparison_of_face_recognition_technologies_/28626598 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biotechnology Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified skin color diversity residual network 18 insufficient generalization ability face image features false detection rate face recognition technology study introduces channel spatial attention mechanism domain attention mechanism face detection study proposes combining channel xlink "> recognition accuracy proposed method potential uses posture changes outcomes indicated lighting changes intricate scenarios current state complex scenes adaptive boosting 64 %, 50 %, |
| dc.title.none.fl_str_mv | Comparison of face recognition technologies. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>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.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_f165c697b8a6fe72efdbb05296d0b8e2 |
| identifier_str_mv | 10.1371/journal.pone.0319921.g009 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28626598 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Comparison of face recognition technologies.Yingying Mei (13817440)BiotechnologyScience PolicySpace ScienceBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedskin color diversityresidual network 18insufficient generalization abilityface image featuresfalse detection rateface recognition technologystudy introduces channelspatial attention mechanismdomain attention mechanismface detectionstudy proposescombining channelxlink ">recognition accuracyproposed methodpotential usesposture changesoutcomes indicatedlighting changesintricate scenarioscurrent statecomplex scenesadaptive boosting64 %,50 %,<div><p>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.</p></div>2025-03-19T17:33:57ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0319921.g009https://figshare.com/articles/figure/Comparison_of_face_recognition_technologies_/28626598CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/286265982025-03-19T17:33:57Z |
| spellingShingle | Comparison of face recognition technologies. Yingying Mei (13817440) Biotechnology Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified skin color diversity residual network 18 insufficient generalization ability face image features false detection rate face recognition technology study introduces channel spatial attention mechanism domain attention mechanism face detection study proposes combining channel xlink "> recognition accuracy proposed method potential uses posture changes outcomes indicated lighting changes intricate scenarios current state complex scenes adaptive boosting 64 %, 50 %, |
| status_str | publishedVersion |
| title | Comparison of face recognition technologies. |
| title_full | Comparison of face recognition technologies. |
| title_fullStr | Comparison of face recognition technologies. |
| title_full_unstemmed | Comparison of face recognition technologies. |
| title_short | Comparison of face recognition technologies. |
| title_sort | Comparison of face recognition technologies. |
| topic | Biotechnology Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified skin color diversity residual network 18 insufficient generalization ability face image features false detection rate face recognition technology study introduces channel spatial attention mechanism domain attention mechanism face detection study proposes combining channel xlink "> recognition accuracy proposed method potential uses posture changes outcomes indicated lighting changes intricate scenarios current state complex scenes adaptive boosting 64 %, 50 %, |