{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T08:48:26Z","timestamp":1780390106639,"version":"3.54.1"},"reference-count":74,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T00:00:00Z","timestamp":1750982400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University of Oregon School of Law, Consumer Protection Research Grant","award":["4236A0"],"award-info":[{"award-number":["4236A0"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Facial authentication has gained widespread adoption as a biometric authentication method, offering a convenient alternative to traditional password-based systems, particularly on mobile devices equipped with front-facing cameras. While this technology enhances usability and security by eliminating password management, it remains highly susceptible to spoofing attacks. Adversaries can exploit facial recognition systems using pre-recorded photos, videos, or even sophisticated 3D models of victims\u2019 faces to bypass authentication mechanisms. The increasing availability of personal images on social media further amplifies this risk, making robust anti-spoofing mechanisms essential for secure facial authentication. To address these challenges, we introduce FaceCloseup, a novel liveness detection technique that strengthens facial authentication by leveraging perspective distortion inherent in close-up shots of real, 3D faces. Instead of relying on additional sensors or user-interactive gestures, FaceCloseup passively analyzes facial distortions in video frames captured by a mobile device\u2019s camera, improving security without compromising user experience. FaceCloseup effectively distinguishes live faces from spoofed attacks by identifying perspective-based distortions across different facial regions. The system achieves a 99.48% accuracy in detecting common spoofing methods\u2014including photo, video, and 3D model-based attacks\u2014and demonstrates 98.44% accuracy in differentiating between individual users. By operating entirely on-device, FaceCloseup eliminates the need for cloud-based processing, reducing privacy concerns and potential latency in authentication. Its reliance on natural device movement ensures a seamless authentication experience while maintaining robust security.<\/jats:p>","DOI":"10.3390\/computers14070254","type":"journal-article","created":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T10:03:48Z","timestamp":1751277828000},"page":"254","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["FaceCloseup: Enhancing Mobile Facial Authentication with Perspective Distortion-Based Liveness Detection"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8256-6988","authenticated-orcid":false,"given":"Yingjiu","family":"Li","sequence":"first","affiliation":[{"name":"Department of Human Physiology, University of Oregon, Eugene, OR 97403, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Aerospace Space and Technology, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zilong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Aerospace Space and Technology, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, Y., Wang, Z., Li, Y., Deng, R.H., Chen, B., Meng, W., and Li, H. 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