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Face recognition under significant p...
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Yang, Feng.
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Face recognition under significant pose variation.
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Face recognition under significant pose variation./
作者:
Yang, Feng.
面頁冊數:
101 p.
附註:
Source: Masters Abstracts International, Volume: 46-01, page: 0414.
Contained By:
Masters Abstracts International46-01.
標題:
Computer Science. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR28958
ISBN:
9780494289587
Face recognition under significant pose variation.
Yang, Feng.
Face recognition under significant pose variation.
- 101 p.
Source: Masters Abstracts International, Volume: 46-01, page: 0414.
Thesis (M.Comp.Sc.)--Concordia University (Canada), 2007.
Unlike the frontal face detection, multi-pose face detection and recognition techniques, still face the following challenges: large variability of environments such as pose, illumination and backgrounds, and unconstrained capturing of facial images. We introduced a new system to deal with this problem. First, a two-step color-based approach is used to find a candidate area of face from original picture. Then a rough estimator of five poses is created using AdaBoost technique. In order to accurately locate the candidate face, multiple statistical shape models-ASM (Active Shape Models) are proposed to estimate an accurate pose of model of the input image and to extract facial features as well. In the recognition step, we use a geometrical mapping technique to deal with the pose variation and face identification.
ISBN: 9780494289587Subjects--Topical Terms:
626642
Computer Science.
Face recognition under significant pose variation.
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Unlike the frontal face detection, multi-pose face detection and recognition techniques, still face the following challenges: large variability of environments such as pose, illumination and backgrounds, and unconstrained capturing of facial images. We introduced a new system to deal with this problem. First, a two-step color-based approach is used to find a candidate area of face from original picture. Then a rough estimator of five poses is created using AdaBoost technique. In order to accurately locate the candidate face, multiple statistical shape models-ASM (Active Shape Models) are proposed to estimate an accurate pose of model of the input image and to extract facial features as well. In the recognition step, we use a geometrical mapping technique to deal with the pose variation and face identification.
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