Face alignment using segmentation and a combined AAM in a PTZ camera

Kwontaeg Choi, Jung Ho Ahn, Hyeran Byun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

In this paper, we propose a novel framework for face alignment based on the Active Appearance Model (AAM) in surveillance systems with Pan-Tilt-Zoom (PTZ) cameras. The AAM converges poorly in face images which are affected by illumination factors, cluttered backgrounds and status of the camera. To search for robust face model parameters, we propose a robust AAM fitting method based on segmenting faces and combining Person-specific and Generic models to achieve accurate face alignment. We segment faces using histogram back-projection and a skin color histogram, which is updated using a skin mask extracted by the AAM. For robust face recognition, we combined Generic and Person-specific models with a slight reduction in processing time. The extracted AAM parameters are as accurate as those when using the Person-specific model and can be used as features for face recognition. Empirical experiments show that our proposed method extracts very accurate face parameters and is not sensitive to initial shapes.

Original languageEnglish
Title of host publicationProceedings - 18th International Conference on Pattern Recognition, ICPR 2006
Pages1191-1194
Number of pages4
DOIs
Publication statusPublished - 2006
Event18th International Conference on Pattern Recognition, ICPR 2006 - Hong Kong, China
Duration: 2006 Aug 202006 Aug 24

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume3
ISSN (Print)1051-4651

Other

Other18th International Conference on Pattern Recognition, ICPR 2006
CountryChina
CityHong Kong
Period06/8/2006/8/24

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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