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Face recognition under significant p...

Yang, Feng.

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Face recognition under significant pose variation.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Face recognition under significant pose variation./
Author:	Yang, Feng.
Description:	101 p.
Notes:	Source: Masters Abstracts International, Volume: 46-01, page: 0414.
Contained By:	Masters Abstracts International46-01.
Subject:	Computer Science. -
Online resource:	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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020 $a 9780494289587
035 $a (UMI)AAIMR28958
035 $a AAIMR28958
040 $a UMI $c UMI
100 1 $a Yang, Feng. $3 1264886
245 1 0 $a Face recognition under significant pose variation.
300 $a 101 p.
500 $a Source: Masters Abstracts International, Volume: 46-01, page: 0414.
502 $a Thesis (M.Comp.Sc.)--Concordia University (Canada), 2007.
520 $a 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.
590 $a School code: 0228.
650 4 $a Computer Science. $3 626642
690 $a 0984
710 2 $a Concordia University (Canada). $3 1018569
773 0 $t Masters Abstracts International $g 46-01.
790 $a 0228
791 $a M.Comp.Sc.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR28958