Mitigation of face image mismatches via deep feature alignment for robust face recognition강인한 얼굴인식을 위한 딥 특징 정렬을 통한 얼굴 영상 불일치 완화에 관한 연구

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dc.contributor.advisorRo, Yong Man-
dc.contributor.advisor노용만-
dc.contributor.authorKim, Hyung-Il-
dc.date.accessioned2022-04-21T19:33:51Z-
dc.date.available2022-04-21T19:33:51Z-
dc.date.issued2021-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=962443&flag=dissertationen_US
dc.identifier.urihttp://hdl.handle.net/10203/295634-
dc.description학위논문(박사) - 한국과학기술원 : 전기및전자공학부, 2021.8,[vii, 73 p. :]-
dc.description.abstractThanks to the advances in deep learning technology, deep learning-based face recognition (FR) has been actively studied, and it shows significantly high performance for various FR benchmark datasets. Accordingly, FR technology has been known as a highly mature technology, and its application to various real-world scenarios is being discussed. However, when the deep learning-based FR algorithm is applied to real-world applications, its performance has been known to be still unsatisfactory. This is mainly attributed to the discrepancy of appearance between training and testing face images. In other words, a face image used for training is usually high-quality, but a face image used for testing is degraded by shooting time, person’s motion, shooting distance between a person and camera, which is referred to as face image mismatch problem. In order to resolve the face image mismatch problem for robust FR, we address the deep feature alignment-based FR algorithm in this dissertation. In particular, we focus on two face image mismatch factors as crucial issues that need to be solved for practical FR: 1) face alignment mismatch (face misalignment) and 2) face image resolution mismatch. In order to deal with the face misalignment problem, we propose a face shape-guided feature alignment learning framework. To tackle the face image resolution mismatch problem, we propose a face image quality-based feature adaptation network between the high-resolution face image, and the low-resolution face image generated by the realistic low-resolution face image generator. Finally, we design a baseline face image quality assessment framework for quantifying the face image mismatch. Through the comparative experiments, we validate the effectiveness of the proposed method under the real-world scenario with the face image mismatch.-
dc.languageeng-
dc.publisher한국과학기술원-
dc.subjectFace recognition▼aDeep learning▼aFace image mismatch▼aFace alignment▼aLow-resolution▼aResolution mismatch▼aFeature alignment-
dc.subject얼굴인식▼a딥러닝▼a얼굴 영상 불일치▼a얼굴정렬▼a저해상도▼a해상도 불일치▼a특징 정렬-
dc.titleMitigation of face image mismatches via deep feature alignment for robust face recognition-
dc.title.alternative강인한 얼굴인식을 위한 딥 특징 정렬을 통한 얼굴 영상 불일치 완화에 관한 연구-
dc.typeThesis(Ph.D)-
dc.identifier.CNRN325007-
dc.description.department한국과학기술원 :전기및전자공학부,-
dc.contributor.alternativeauthor김형일-
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