Active learning based automatic face segmentation for kinect video

This paper presents a novel segmentation approach for extracting faces from videos. Under an active learning framework, the segmentation is conducted automatically without human interactions. A small portion of pixels are first labeled as face or non-face. Given these labeled samples, a semi-supervi...

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Bibliographic Details
Main Authors: Jixia Zhang, Haibo Wang, Shaoguo Liu, Davoine, Franck, Chunhong Pan, Shiming Xiang
Format: Conference Proceeding
Language:English
Subjects:
Detectors
Face
Image color analysis
Image segmentation
Labeling
Robustness
Skin
Online Access:Request full text
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Description
Summary:This paper presents a novel segmentation approach for extracting faces from videos. Under an active learning framework, the segmentation is conducted automatically without human interactions. A small portion of pixels are first labeled as face or non-face. Given these labeled samples, a semi-supervised spline regression model is then applied to obtain the face region. Based on the segmentation result, new pixels are selected and labeled. These two steps perform iterately until convergence. The main novelty is that color and depth data are combined to provide the labeling information. Our approach is validated via comparisons with state-of-the-art methods on real videos captured from the commodity Kinect camera.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2013.6637966