DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Ro, Yong Man | - |
dc.contributor.advisor | 노용만 | - |
dc.contributor.author | Kim, Hyung-Il | - |
dc.date.accessioned | 2022-04-21T19:33:51Z | - |
dc.date.available | 2022-04-21T19:33:51Z | - |
dc.date.issued | 2021 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=962443&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/295634 | - |
dc.description | 학위논문(박사) - 한국과학기술원 : 전기및전자공학부, 2021.8,[vii, 73 p. :] | - |
dc.description.abstract | Thanks 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.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Face recognition▼aDeep learning▼aFace image mismatch▼aFace alignment▼aLow-resolution▼aResolution mismatch▼aFeature alignment | - |
dc.subject | 얼굴인식▼a딥러닝▼a얼굴 영상 불일치▼a얼굴정렬▼a저해상도▼a해상도 불일치▼a특징 정렬 | - |
dc.title | Mitigation of face image mismatches via deep feature alignment for robust face recognition | - |
dc.title.alternative | 강인한 얼굴인식을 위한 딥 특징 정렬을 통한 얼굴 영상 불일치 완화에 관한 연구 | - |
dc.type | Thesis(Ph.D) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :전기및전자공학부, | - |
dc.contributor.alternativeauthor | 김형일 | - |
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