{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:09:15Z","timestamp":1753884555140,"version":"3.41.2"},"reference-count":36,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2018,6,25]],"date-time":"2018-06-25T00:00:00Z","timestamp":1529884800000},"content-version":"vor","delay-in-days":175,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003787","name":"Natural Science Foundation of Hebei Province","doi-asserted-by":"publisher","award":["F2018201115","F2018201096"],"award-info":[{"award-number":["F2018201115","F2018201096"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003482","name":"Department of Education of Hebei Province","doi-asserted-by":"publisher","award":["QN2015026","QN2017019"],"award-info":[{"award-number":["QN2015026","QN2017019"]}],"id":[{"id":"10.13039\/501100003482","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2018,1]]},"abstract":"<jats:p>Face representation and matching are two essential issues in face verification task. Various approaches have been proposed focusing on these two issues. However, few of them addressed the joint optimal solutions of these two issues in a unified framework. In this paper, we present a second\u2010order face representation method for face pair and a unified face verification framework, in which the feature extractors and the subsequent binary classification model design can be selected flexibly. Our contributions can be summarized in the following aspects. First, a novel face\u2010pair representation method that employs the second\u2010order statistical property of the face pairs is proposed, which retains more information compared to the existing methods. Second, a flexible binary classification model, which differs from the conventionally used metric learning, is constructed based on the new face\u2010pair representation. Finally, we verify that our proposed face\u2010pair representation can benefit from large training datasets. 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