{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:44:40Z","timestamp":1760060680923,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T00:00:00Z","timestamp":1758153600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Project of 2025 Hunan Provincial Social Science Achievement Review Committee","award":["XSP25YBZ053"],"award-info":[{"award-number":["XSP25YBZ053"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Face recognition under uneven illumination conditions presents significant challenges, as asymmetric shadows often obscure facial features while overexposed regions lose critical texture details. To address this problem, a novel symmetry-aware illumination enhancement method named face shadow detection network (FSDN) is proposed, which features a nested U-Net architecture combined with Gaussian convolution. This method enables precise illumination intensity maps for the given face images through higher-order quadratic enhancement curves, effectively extending the low-light dynamic range while preserving essential facial symmetry. Comprehensive evaluations on the Extended Yale B and CMU-PIE datasets demonstrate the superiority of the proposed FSDN over conventional approaches, achieving structural similarity (SSIM) indices of 0.48 and 0.59, respectively, along with remarkably low face recognition error rates of 1.3% and 0.2%, respectively. The key innovation of this work lies in its simultaneous optimization of illumination uniformity and facial symmetry preservation, thereby significantly improving face analysis reliability under challenging lighting conditions.<\/jats:p>","DOI":"10.3390\/sym17091560","type":"journal-article","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T09:32:32Z","timestamp":1758187952000},"page":"1560","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Symmetry-Aware Face Illumination Enhancement via Pixel-Adaptive Curve Mapping"],"prefix":"10.3390","volume":"17","author":[{"given":"Jieqiong","family":"Yang","sequence":"first","affiliation":[{"name":"School of Mathematics and Physics, Hunan Institute of Technology, Hengyang 421002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yumeng","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Advanced Interdisciplinary Studies, Central South University of Forestry and Technology, Changsha 410004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaqi","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Advanced Interdisciplinary Studies, Central South University of Forestry and Technology, Changsha 410004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jizheng","family":"Yi","sequence":"additional","affiliation":[{"name":"College of Advanced Interdisciplinary Studies, Central South University of Forestry and Technology, Changsha 410004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1109\/34.598229","article-title":"Face recognition: The problem of compensating for changes in illumination direction","volume":"19","author":"Adini","year":"1997","journal-title":"IEEE Trans. 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