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
- 2006.11
- 수록면
- 630 - 633 (4page)
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초록· 키워드
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Eye localization is necessary for face recognition and related application areas. Most of eye localization algorithms reported thus far still need to be improved about precision and computational time for successful applications.
In this paper, we propose an eye location method based on gabor feature vectors which is more robust with respect to initial points. The proposed method first makes an eye model bunch using gabor feature vector and eye coordinates of each eyes obtained in model face images, and then tries to localize eyes in an incoming face image by utilizing the fact that the true eye coordinates is most likely to be very close to the eye coordinates associated with a gabor feature vector in the eye model bunch with which the gabor feature vector at the true eye coordinates will have the best matching similarity relation Similar ideas have been already studied as in EBGM. However, the method used in EBGM is known to be fragile to initial values and may need extensive search range for achieving reasonably good precision, but extensive search ranges will cause more computational burden. In this paper, we propose a multiscale approach without increasing computational burden where one first tries to locate eyes by using gabor similarity in a coarse face image obtained from down sampling of the original face image, and then locate eyes in the original resolution face image by using the coarse eye coordinates as an intial point. Several experiments show the efficiency of our proposed method.
In this paper, we propose an eye location method based on gabor feature vectors which is more robust with respect to initial points. The proposed method first makes an eye model bunch using gabor feature vector and eye coordinates of each eyes obtained in model face images, and then tries to localize eyes in an incoming face image by utilizing the fact that the true eye coordinates is most likely to be very close to the eye coordinates associated with a gabor feature vector in the eye model bunch with which the gabor feature vector at the true eye coordinates will have the best matching similarity relation Similar ideas have been already studied as in EBGM. However, the method used in EBGM is known to be fragile to initial values and may need extensive search range for achieving reasonably good precision, but extensive search ranges will cause more computational burden. In this paper, we propose a multiscale approach without increasing computational burden where one first tries to locate eyes by using gabor similarity in a coarse face image obtained from down sampling of the original face image, and then locate eyes in the original resolution face image by using the coarse eye coordinates as an intial point. Several experiments show the efficiency of our proposed method.
목차
Abstract
Ⅰ. 서론
Ⅱ. 가버특징 벡터 기반 눈좌표 검출
Ⅲ. 멀티스케일 가버특징기반 눈좌표검출
Ⅳ. 실험 및 결과 검토
Ⅴ. 결론
참고문헌
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