지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2014.11
- 수록면
- 392 - 395 (4page)
이용수
초록· 키워드
오류제보하기
Face recognition has been widely investigated in the last decade. However, real world application for face recognition is still a challenge. Most of these face recognition algorithms are under controlled settings, such as limited viewpoint and illumination changes. In this paper, we focus on face recognition which tolerates large viewpoint change. A novel framework named Orientation based Scale Invariant Feature Transform (OSIFT) is proposed. OSIFT is an extension of SIFT algorithm. SIFT is a scale and rotation invariant algorithm, which is powerful for small viewpoint changes in face recognition, but it fails when large viewpoint change exists. To handle this problem, we propose to use Lucas-Kanade algorithm to generate different viewpoint face from a single frontal face. After that, SIFT is used to detect local features from these viewpoints, these SIFT features contain information of different viewpoint face, which can deal with the problem of face viewpoint change. Finally, our framework is compared with the SIFT algorithm and other similar solutions. Experiment results show our framework achieves better recognition accuracy than SIFT algorithm.
목차
Abstract
Ⅰ. Introduction
Ⅱ. OSIFT
Ⅲ. Results
Ⅳ. Conclusions
References
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UCI(KEPA) : I410-ECN-0101-2016-569-001270030