{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T15:33:29Z","timestamp":1774539209325,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,15]],"date-time":"2021-04-15T00:00:00Z","timestamp":1618444800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The emergence of biometric-based authentication using modern sensors on electronic devices has led to an escalated use of face recognition technologies. While these technologies may seem intriguing, they are accompanied by numerous implicit drawbacks. In this paper, we look into the problem of face anti-spoofing (FAS) on a frame level in an attempt to ameliorate the risks of face-spoofed attacks in biometric authentication processes. We employed a bi-directional feature pyramid network (BiFPN) that is used for convolutional multi-scaled feature extraction on the EfficientDet detection architecture, which is novel to the task of FAS. We further use these convolutional multi-scaled features in order to perform deep pixel-wise supervision. For all of our experiments, we performed evaluations across all major datasets and attained competitive results for the majority of the cases. Additionally, we showed that introducing an auxiliary self-supervision branch tasked with reconstructing the inputs in the frequency domain demonstrates an average classification error rate (ACER) of 2.92% on Protocol IV of the OULU-NPU dataset, which is significantly better than the currently available published works on pixel-wise face anti-spoofing. Moreover, following the procedures of prior works, we performed inter-dataset testing, which further consolidated the generalizability of the proposed models, as they showed optimum results across various sensors without any fine-tuning procedures.<\/jats:p>","DOI":"10.3390\/s21082799","type":"journal-article","created":{"date-parts":[[2021,4,15]],"date-time":"2021-04-15T21:35:13Z","timestamp":1618522513000},"page":"2799","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Bi-FPNFAS: Bi-Directional Feature Pyramid Network for Pixel-Wise Face Anti-Spoofing by Leveraging Fourier Spectra"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9525-7196","authenticated-orcid":false,"given":"Koushik","family":"Roy","sequence":"first","affiliation":[{"name":"Gaze Pte. Ltd., Singapore 068914, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3624-6534","authenticated-orcid":false,"given":"Md.","family":"Hasan","sequence":"additional","affiliation":[{"name":"Gaze Pte. Ltd., Singapore 068914, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9971-7033","authenticated-orcid":false,"given":"Labiba","family":"Rupty","sequence":"additional","affiliation":[{"name":"Gaze Pte. Ltd., Singapore 068914, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7436-9925","authenticated-orcid":false,"given":"Md. Sourave","family":"Hossain","sequence":"additional","affiliation":[{"name":"Gaze Pte. Ltd., Singapore 068914, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5604-0082","authenticated-orcid":false,"given":"Shirshajit","family":"Sengupta","sequence":"additional","affiliation":[{"name":"Gaze Pte. Ltd., Singapore 068914, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6943-8102","authenticated-orcid":false,"given":"Shehzad Noor","family":"Taus","sequence":"additional","affiliation":[{"name":"Gaze Pte. Ltd., Singapore 068914, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7661-3570","authenticated-orcid":false,"given":"Nabeel","family":"Mohammed","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, North South University, Dhaka 1229, Bangladesh"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Xue, N., and Zafeiriou, S. (2019, January 15\u201320). ArcFace: Additive Angular Margin Loss for Deep Face Recognition. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00482"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, H., Wang, Y., Zhou, Z., Ji, X., Gong, D., Zhou, J., Li, Z., and Liu, W. 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