Premium
Learning‐based 3D face detection using geometric context
Author(s) -
Guo Yanwen,
Zhang Fuyan,
Liu Chunxiao,
Sun Hanqiu,
Peng Qunsheng
Publication year - 2007
Publication title -
computer animation and virtual worlds
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.225
H-Index - 49
eISSN - 1546-427X
pISSN - 1546-4261
DOI - 10.1002/cav.192
Subject(s) - computer science , artificial intelligence , discriminative model , face detection , classifier (uml) , adaboost , facial recognition system , computer graphics , object class detection , computer vision , pattern recognition (psychology) , face (sociological concept) , machine learning , social science , sociology
In computer graphics community, face model is one of the most useful entities. The automatic detection of 3D face model has special significance to computer graphics, vision, and human‐computer interaction. However, few methods have been dedicated to this task. This paper proposes a machine learning approach for fully automatic 3D face detection. To exploit the facial features, we introduce geometric context , a novel shape descriptor which can compactly encode the distribution of local geometry and can be evaluated efficiently by using a new volume encoding form, named integral volume . Geometric contexts over 3D face offer the rich and discriminative representation of facial shapes and hence are quite suitable to classification. We adopt an AdaBoost learning algorithm to select the most effective geometric context‐based classifiers and to combine them into a strong classifier. Given an arbitrary 3D model, our method first identifies the symmetric parts as candidates with a new reflective symmetry detection algorithm. Then uses the learned classifier to judge whether the face part exists. Experiments are performed on a large set of 3D face and non‐face models and the results demonstrate high performance of our method. Copyright © 2007 John Wiley & Sons, Ltd.