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Recognizing age\u2010separated face images is still a challenging research problem due to complex aging processes involving different types of facial tissues, skin, fat, muscles, and bones. Certain holistic and local facial features are used to recognize age\u2010separated face images. However, most of the existing methods recognize face images without incorporating the knowledge learned from age group estimation. In this paper, we propose an age\u2010assisted face recognition approach to handle aging variations. Inspired by the observation that facial asymmetry is an age\u2010dependent intrinsic facial feature, we first use asymmetric facial dimensions to estimate the age group of a given face image. Deeply learned asymmetric facial features are then extracted for face recognition using a deep convolutional neural network (\n                    <jats:styled-content style=\"fixed-case\">dCNN<\/jats:styled-content>\n                    ). Finally, we integrate the knowledge learned from the age group estimation into the face recognition algorithm using the same\n                    <jats:styled-content style=\"fixed-case\">dCNN<\/jats:styled-content>\n                    . This integration results in a significant improvement in the overall performance compared to using the face recognition algorithm alone. The experimental results on two large facial aging datasets, the\n                    <jats:styled-content style=\"fixed-case\">MORPH<\/jats:styled-content>\n                    and\n                    <jats:styled-content style=\"fixed-case\">FERET<\/jats:styled-content>\n                    sets, show that the proposed age group estimation based on the face recognition approach yields superior performance compared to some existing state\u2010of\u2010the\u2010art methods.\n                  <\/jats:p>","DOI":"10.1111\/1556-4029.13798","type":"journal-article","created":{"date-parts":[[2018,4,23]],"date-time":"2018-04-23T17:32:39Z","timestamp":1524504759000},"page":"1727-1749","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Facial Asymmetry\u2010Based Age Group Estimation: Role in Recognizing Age\u2010Separated Face Images"],"prefix":"10.1111","volume":"63","author":[{"given":"Muhammad","family":"Sajid","sequence":"first","affiliation":[{"name":"Vision and Pattern Recognition Systems Research Group Capital University of Science and Technology Expressway, Zone V Islamabad Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Imtiaz Ahmad","family":"Taj","sequence":"additional","affiliation":[{"name":"Vision and Pattern Recognition Systems Research Group Capital University of Science and Technology Expressway, Zone V Islamabad Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5755-1194","authenticated-orcid":false,"given":"Usama Ijaz","family":"Bajwa","sequence":"additional","affiliation":[{"name":"Department of Computer Science COMSATS Institute of Information Technology Off Raiwind Road Lahore Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naeem Iqbal","family":"Ratyal","sequence":"additional","affiliation":[{"name":"Vision and Pattern Recognition Systems Research Group Capital University of Science and Technology Expressway, Zone V Islamabad Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2018,4,23]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2008.921847"},{"key":"e_1_2_9_3_1","doi-asserted-by":"crossref","unstructured":"YangZ AiH.Demographic classification with local binary patterns.Proceedings of the International Conference on Biometrics: Advances in Biometrics; 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