Face Segmentation under Unconstrained Scenes

Author

Wang, Jing-Wein

Author_Institution

Inst. of Photonics & Commun., Nat. Kaohsiung Univ. of Appl. Sci.

fYear

2006

fDate

9-12 July 2006

Firstpage

1833

Lastpage

1836

Abstract

In this paper, an efficient approach by combining the novel wavelet-based feature template, the support vector machine (SVM) classifier, and the wavelet entropy filtering is presented to robustly detect and segment human face image under complex background. Moreover, a face detection measure (FDM) criterion based on the distance between the expected and the detected eye-mouth triangle circumscribed circle areas is introduced to validate the performance of precise face segmentation

Keywords

face recognition; feature extraction; filtering theory; image classification; image segmentation; object detection; support vector machines; wavelet transforms; FDM; SVM classifier; eye-mouth triangle; face detection measure; face segmentation; support vector machine; unconstrained scene; wavelet entropy filtering; wavelet-based feature template; Area measurement; Entropy; Face detection; Filtering; Humans; Image segmentation; Layout; Robustness; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Multimedia and Expo, 2006 IEEE International Conference on

Conference_Location

Toronto, Ont.

Print_ISBN

1-4244-0366-7

Electronic_ISBN

1-4244-0367-7

Type

conf

DOI

10.1109/ICME.2006.262910

Filename

4036979

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2958610