{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T17:02:29Z","timestamp":1769706149196,"version":"3.49.0"},"reference-count":27,"publisher":"SAGE Publications","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,11,4]]},"abstract":"<jats:p>Aiming at the challenges that the traditional photoplethysmography (PPG) biometrics is not robust and precision of recognition, this paper proposes a dual-feature and multi-scale fusion using U2-net deep learning model (DMFUDM). First, to obtain complementary information of different features, we extract the local and global features of one-dimensional multi-resolution local binary patterns (1DMRLBP) and multi-scale differential feature (MSDF). Then, to extract robust discriminant feature information from the 1DMRLBP and MSDF features, a novel two-branch U2-net framework is constructed. In addition, a multi-scale extraction module is designed to capture the transition information. It consists of multiple convolution layers with different receptive fields for capturing multi-scale transition information. At last, a two-level attention module is used to adaptively capture valuable information for ECG biometrics. DMFUDM can obtain the average subject recognition rates of 99.76%, 98.31%, 98.97% and 98.87% on four databases, respectively, and experiment results show that it performs competitively with state-of-the-art methods on all four databases.<\/jats:p>","DOI":"10.3233\/jifs-230721","type":"journal-article","created":{"date-parts":[[2023,8,13]],"date-time":"2023-08-13T15:07:22Z","timestamp":1691939242000},"page":"7445-7454","source":"Crossref","is-referenced-by-count":0,"title":["Dual-feature and multi-scale fusion using U2-net deep learning model for ECG biometric recognition"],"prefix":"10.1177","volume":"45","author":[{"given":"Zunmei","family":"Hu","sequence":"first","affiliation":[{"name":"School of Computer, Heze University, Heze, Shandong Province, China"}]},{"given":"Yuwen","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer, Heze University, Heze, Shandong Province, 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