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This study aims to address a broader problem known as variation-invariant face recognition (VIFR) by exploring the question: \u201cHow can identity preservation be maximized in the presence of TD and TID variations?\" While existing state-of-the-art (SOTA) methods focus on either age-invariant or race and sex-invariant FR, our approach introduces the first novel deep learning architecture utilizing multi-task learning to tackle VIFR, termed \u201cmulti-task learning-based variation-invariant face recognition (MTLVIFR).\" We redefine FR by incorporating both TD and TID, decomposing faces into age (TD) and residual features (TID: sex, race, and identity). MTLVIFR outperforms existing methods by 2% in LFW and CALFW benchmarks, 1% in CALFW, and 5% in AgeDB (20 years of protocol) in terms of face verification score. Moreover, it achieves higher face identification scores compared to all SOTA methods. Open source\u00a0<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/a-haider1992\/vifr.git\">code<\/jats:ext-link>.<\/jats:p>","DOI":"10.1007\/s00521-024-10234-x","type":"journal-article","created":{"date-parts":[[2024,8,11]],"date-time":"2024-08-11T12:01:59Z","timestamp":1723377719000},"page":"20147-20166","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Residual feature decomposition and multi-task learning-based variation-invariant face recognition"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1802-3086","authenticated-orcid":false,"given":"Abbas","family":"Haider","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanfeng","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ivor","family":"Spence","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,8,11]]},"reference":[{"key":"10234_CR1","unstructured":"Auli M, Galley M, Quirk C et\u00a0al (2013) Joint language and translation modeling with recurrent neural networks. 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