FaceSeg: Automatic Face Segmentation for Real-Time Video

Segmenting human faces automatically is very important for face recognition and verification, security system, and computer vision. In this paper, we present an accurate segmentation system for cutting human faces out from video sequences in real-time. First, a learning based face detector is develo...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on multimedia 2009-01, Vol.11 (1), p.77-88
Main Authors: Hongliang Li, Ngan, K.N., Qiang Liu
Format: Article
Language:English
Subjects:
AdaBoost
Algorithms
Applied sciences
Artificial intelligence
Cascades
Coarsening
Computer science
control theory
systems
Computer systems and distributed systems. User interface
Computer vision
Cutting
Data processing. List processing. Character string processing
Detectors
Exact sciences and technology
Face detection
graph cut
Human
Humans
Image segmentation
matting
Memory and file management (including protection and security)
Memory organisation. Data processing
Object detection
Pattern recognition
Pattern recognition. Digital image processing. Computational geometry
Real time
Real time systems
Segmentation
Software
Video sequences
Videoconference
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Segmenting human faces automatically is very important for face recognition and verification, security system, and computer vision. In this paper, we present an accurate segmentation system for cutting human faces out from video sequences in real-time. First, a learning based face detector is developed to rapidly find human faces. To speed up the detection process, a face rejection cascade is constructed to remove most of negative samples while retaining all the face samples. Then, we develop a coarse-to-fine segmentation approach to extract the faces based on a min-cut optimization. Finally, a new matting algorithm is proposed to estimate the alpha-matte based on an adaptive trimap generation method. Experimental results demonstrate the effectiveness and robustness of our proposed method that can compete with the well-known interactive methods in real-time.
ISSN:1520-9210
1941-0077
DOI:10.1109/TMM.2008.2008922