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Face recognition under significant pose variation


Face recognition under significant pose variation

Yang, Feng (2007) Face recognition under significant pose variation. Masters thesis, Concordia University.

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MR28958.pdf - Accepted Version


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.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Computer Science and Software Engineering
Item Type:Thesis (Masters)
Authors:Yang, Feng
Pagination:x, 101 leaves ; 29 cm.
Institution:Concordia University
Degree Name:M. Comp. Sc.
Program:Computer Science and Software Engineering
Thesis Supervisor(s):Zhang, J
Identification Number:LE 3 C66C67M 2007 Y36
ID Code:975540
Deposited By: Concordia University Library
Deposited On:22 Jan 2013 16:10
Last Modified:13 Jul 2020 20:08
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