{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T21:12:41Z","timestamp":1762377161947,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,12]],"date-time":"2022-05-12T00:00:00Z","timestamp":1652313600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"\u201cGreen Valley Lean\u201d Talent Project (2020) of Lishui, Zhejiang Province, China","award":["KYY-HX-20190956"],"award-info":[{"award-number":["KYY-HX-20190956"]}]},{"name":"Key Technology R&amp;D Project of Power Monitoring System Based on Cloud Computing of Zhejiang University of Technology, China","award":["KYY-HX-20190956"],"award-info":[{"award-number":["KYY-HX-20190956"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the field of biometric recognition, finger vein recognition has received widespread attention by virtue of its advantages, such as biopsy, which is not easy to be stolen. However, due to the limitation of acquisition conditions such as noise and illumination, as well as the limitation of computational resources, the discriminative features are not comprehensive enough when performing finger vein image feature extraction. It will lead to such a result that the accuracy of image recognition cannot meet the needs of large numbers of users and high security. Therefore, this paper proposes a novel feature extraction method called principal component local preservation projections (PCLPP). It organically combines principal component analysis (PCA) and locality preserving projections (LPP) and constructs a projection matrix that preserves both the global and local features of the image, which will meet the urgent needs of large numbers of users and high security. In this paper, we apply the Shandong University homologous multi-modal traits (SDUMLA-HMT) finger vein database to evaluate PCLPP and add \u201cSalt and pepper\u201d noise to the dataset to verify the robustness of PCLPP. The experimental results show that the image recognition rate after applying PCLPP is much better than the other two methods, PCA and LPP, for feature extraction.<\/jats:p>","DOI":"10.3390\/s22103691","type":"journal-article","created":{"date-parts":[[2022,5,12]],"date-time":"2022-05-12T23:08:36Z","timestamp":1652396916000},"page":"3691","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Finger Vein Feature Extraction Method Incorporating Principal Component Analysis and Locality Preserving Projections"],"prefix":"10.3390","volume":"22","author":[{"given":"Dingzhong","family":"Feng","sequence":"first","affiliation":[{"name":"College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China"},{"name":"Zhejiang Jinghong Intelligent Technology Co., Ltd., Jinyun 321400, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shanyu","family":"He","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zihao","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ye","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China"},{"name":"Zhejiang Jinghong Intelligent Technology Co., Ltd., Jinyun 321400, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"15233","DOI":"10.1007\/s11042-021-10548-1","article-title":"Hybrid local phase quantization and grey wolf optimization based SVM for finger vein recognition","volume":"80","author":"Kapoor","year":"2021","journal-title":"Multimed. 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