An SVM-PCA-based method of human face ROI localization

Author

Meng, Shan ; Huang, Jingsiong ; Zhang, Youwei

Author_Institution

Coll. of Inf. Eng., Shenzhen Univ., China

Volume

2

fYear

2004

fDate

31 Aug.-4 Sept. 2004

Firstpage

963

Abstract

Human face Region of Interest (ROI) localization is a prerequisite step for face recognition and lip reading application. This paper describes a ROI localization method based on Support Vector Machine (SVM) and Principle Component Analysis (PCA). We use simplified skin color model to segment input images. In the estimated face area, we first make rough classification with two-class SVM and then we localize the facial ROI by minimizing PCA reconstruction error, also called Distance From Feature Space (DFFS). Results are presented on images from Chinese Audio-Visual Speech Database (CAVSD).

Keywords

face recognition; feature extraction; image classification; image colour analysis; image reconstruction; image segmentation; principal component analysis; support vector machines; CAVSD; Chinese audio-visual speech database; DFFS; PCA reconstruction error; ROI; SVM; distance from feature space; face recognition; human face localization; image classification; image segmentation; lip reading application; minimization; principle component analysis; region of interest; skin color model; support vector machine; Face recognition; Humans; Image databases; Image reconstruction; Image segmentation; Principal component analysis; Skin; Speech; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing, 2004. Proceedings. ICSP '04. 2004 7th International Conference on

Print_ISBN

0-7803-8406-7

Type

conf

DOI

10.1109/ICOSP.2004.1441480

Filename

1441480