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Syst."],"published-print":{"date-parts":[[2024,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The significance of facial anti-spoofing algorithms in enhancing the security of facial recognition systems cannot be overstated. Current approaches aim to compensate for the model\u2019s shortcomings in capturing spatial information by leveraging spatio-temporal information from multiple frames. However, the additional branches to extract inter-frame details increases the model\u2019s parameter count and computational workload, leading to a decrease in inference efficiency. To address this, we have developed a robust and easily deployable facial anti-spoofing algorithm. In this paper, we propose Central Difference Convolution UNet++ (UCDCN), which takes advantage of central difference convolution and improves the characterization ability of invariant details in diverse environments. Particularly, we leverage domain knowledge from image segmentation and propose a multi-level feature fusion network structure to enhance the model\u2019s ability to capture semantic information which is beneficial for face anti-spoofing tasks. In this manner, UCDCN greatly reduces the number of model parameters as well as achieves satisfactory metrics on three popular benchmarks, i.e., Replay-Attack, Oulu-NPU and SiW.<\/jats:p>","DOI":"10.1007\/s40747-024-01397-0","type":"journal-article","created":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T09:01:43Z","timestamp":1712653303000},"page":"4817-4833","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["UCDCN: a nested architecture based on central difference convolution for face anti-spoofing"],"prefix":"10.1007","volume":"10","author":[{"given":"Jing","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Quanhao","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Xiangzhou","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Ruqian","family":"Hao","sequence":"additional","affiliation":[]},{"given":"Xiaohui","family":"Du","sequence":"additional","affiliation":[]},{"given":"Siying","family":"Tao","sequence":"additional","affiliation":[]},{"given":"Juanxiu","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6523-1665","authenticated-orcid":false,"given":"Lin","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,9]]},"reference":[{"key":"1397_CR1","unstructured":"Jukka M, Abdenour H, Matti P (2011) Face spoofing detection from single images using micro-texture analysis. 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