Classifying Facial Gestures in Presence of Head Motion (bibtex)
by Liao, Wei-Kai and Cohen, Isaac
Abstract:
This paper addresses the problem of automatic facial gestures recognition in an interactive environment. Automatic facial gestures recognition is a difficult problem in computer vision, and most of the work has focused on inferring facial gestures in the context of a static head. In the paper we address the challenging problem of recognizing the facial expressions of a moving head. We present a systematic framework to analyze and classify the facial gestures with the head movement. Our system includes a 3D head pose estimation method to recover the global head motion. After estimating the head pose, the human face is modeled by a collection of face's regions. These regions represent the face model used for locating and extracting temporal facial features. We propose using a locally affine motion model to represent extracted motion fields. The classification consists of a graphical model for robustly representing the dependencies of the selected facial regions and the support vector machine. Our experiments show that this approach could classify human expressions in interactive environments accurately.
Reference:
Classifying Facial Gestures in Presence of Head Motion (Liao, Wei-Kai and Cohen, Isaac), In IEEE Workshop on Vision for Human-Computer Interaction, 2005.
Bibtex Entry:
@inproceedings{liao_classifying_2005,
	address = {San Diego, CA},
	title = {Classifying {Facial} {Gestures} in {Presence} of {Head} {Motion}},
	url = {http://ict.usc.edu/pubs/Classifying%20Facial%20Gestures%20in%20Presence%20of%20Head%20Motion.pdf},
	abstract = {This paper addresses the problem of automatic facial gestures recognition in an interactive environment. Automatic facial gestures recognition is a difficult problem in computer vision, and most of the work has focused on inferring facial gestures in the context of a static head. In the paper we address the challenging problem of recognizing the facial expressions of a moving head. We present a systematic framework to analyze and classify the facial gestures with the head movement. Our system includes a 3D head pose estimation method to recover the global head motion. After estimating the head pose, the human face is modeled by a collection of face's regions. These regions represent the face model used for locating and extracting temporal facial features. We propose using a locally affine motion model to represent extracted motion fields. The classification consists of a graphical model for robustly representing the dependencies of the selected facial regions and the support vector machine. Our experiments show that this approach could classify human expressions in interactive environments accurately.},
	booktitle = {{IEEE} {Workshop} on {Vision} for {Human}-{Computer} {Interaction}},
	author = {Liao, Wei-Kai and Cohen, Isaac},
	month = jun,
	year = {2005}
}
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