{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T12:10:08Z","timestamp":1775218208658,"version":"3.50.1"},"reference-count":41,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:00:00Z","timestamp":1760486400000},"content-version":"vor","delay-in-days":287,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Image Processing"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>In order to better extract the discriminative features of palmprint images and improve the accuracy of palmprint recognition, this paper proposes a palmprint feature fusion recognition algorithm based on Gabor and Conformer. First, Gabor filtering is applied to the palmprint region image to obtain the global distribution feature vector of image texture features. Meanwhile, a deep network feature extractor based on the Conformer neural network is constructed to obtain the local distribution feature vector of image deep features. Then, the kernel canonical correlation analysis (KCCA) method is used to extract the correlation features between the two groups of feature vectors as effective discriminative information, eliminate the information redundancy between the features, and obtain more accurate palmprint fusion features. Finally, a knowledge graph is used to pre\u2010classify the palmprint fusion features to reduce the feature search space, and the cosine distance classifier is used to recognise the palmprint. Simulation results show that the accuracy of the proposed method improves by about 15.02% compared with the basic features and about 3.92% compared with other algorithms, which proves the effectiveness of the proposed algorithm. The main contributions of this work lie in the joint encoding of global Gabor texture features and Conformer\u2010based local\u2013global representations, the correlation\u2010aware fusion via KCCA, and the acceleration of large\u2010scale recognition through a knowledge\u2010graph\u2010based two\u2010stage matching mechanism.<\/jats:p>","DOI":"10.1049\/ipr2.70222","type":"journal-article","created":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T19:35:36Z","timestamp":1760556936000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Palmprint Features Fusion Recognition Based on Conformer and Gabor"],"prefix":"10.1049","volume":"19","author":[{"given":"Xiancheng","family":"Zhou","sequence":"first","affiliation":[{"name":"Xiangjiang Laboratory  Changsha China"},{"name":"School of Intelligent Engineering and Intelligent Manufacturing Hunan University of Technology and Business  Changsha China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9015-2114","authenticated-orcid":false,"given":"Yulong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Intelligent Engineering and Intelligent Manufacturing Hunan University of Technology and Business  Changsha China"}]},{"given":"Xinran","family":"Ji","sequence":"additional","affiliation":[{"name":"School of Intelligent Engineering and Intelligent Manufacturing Hunan University of Technology and Business  Changsha China"}]},{"given":"Kaijun","family":"Zhou","sequence":"additional","affiliation":[{"name":"Xiangjiang Laboratory  Changsha China"},{"name":"School of Intelligent Engineering and Intelligent Manufacturing Hunan University of Technology and Business  Changsha China"}]}],"member":"265","published-online":{"date-parts":[[2025,10,15]]},"reference":[{"issue":"3","key":"e_1_2_9_2_1","first-page":"920","article-title":"Palmprint Recognition Based on MB\u2010LBP and HOG","volume":"34","author":"Cen Y.","year":"2017","journal-title":"Computer Applications and Research"},{"issue":"5","key":"e_1_2_9_3_1","first-page":"515","article-title":"Palmprint Recognition Based on Hybrid Gabor Filter and Weighted Centrosymmetric LBP","volume":"32","author":"Lin S.","year":"2021","journal-title":"Journal of Optoelectronics Laser"},{"issue":"16","key":"e_1_2_9_4_1","first-page":"316","article-title":"Palmprint Recognition Based on Multi\u2010Scale Gabor Direction Weber Local Descriptor","volume":"58","author":"Li M.","year":"2021","journal-title":"Advances in Laser and Optoelectronics"},{"issue":"6","key":"e_1_2_9_5_1","first-page":"1361","article-title":"Palmprint Recognition Based on Feature Weighting and Kernel Principal Component Analysis","volume":"54","author":"Gao L.","year":"2016","journal-title":"Journal of Jilin University (Science Edition)"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.07.083"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1049\/iet-cvi.2019.0200"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.11834\/jig.180605"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108247"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.107071"},{"key":"e_1_2_9_11_1","first-page":"41","article-title":"Boosting Palmprint Identification With Gender Information Using DeepNet. 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