COMPUTER ANIMATION AND VIRTUAL WORLDS Comp.Anim.Virtual Worlds 2007;18:483-492 WILEY Published online 2 July 2007 in Wiley InterScience InterScience (www.interscience.wiley.com)DOI:10.1002/cav.192 年泰年年●●市市泰●泰泰年年年布市市 Learning-based 3D face detection using geometric context By Yanwen Guo',Fuyan Zhang,Chunxiao Liu,Hanqiu Sun and Qunsheng Peng ................ 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. Received:15 May 2007;Accepted:15 May 2007 KEY WORDS:3D face model;face detection;geometric context;AdaBoost learning Introduction if the face part exists,locating its position on the model surface.This technology is very meaningful.For instance, For the wide applications in biometric identification, when producing new characters for animation,it is often face tracking,and human computer interaction,2D face necessary to search for the available 3D faces and human detection and recognition from images were intensively models in databases or on web as reference to avoid re- explored in the past decades.Many methods have been scanning and re-modeling.Furthermore,the automatic brought forward so far.1-5 Since 2D image is prone detection of 3D face model will also facilitate 3D face to variations of pose,expression,and illumination,the recognition,s biometric identification,?automatic texture robust and efficient techniques are still challenging.With mapping,8 and so on. the fast development of 3D scanning techniques,3D 3D face detection involves similar issue as model model retrieval is becoming convenient.In contrast with retrieval,which generally refers to searching models 2D image,3D model normally contains more inherent similar to the input one from database.Current methods information for special modalities.People thus attempt of model retrieval concentrate on matching global to seek the solution using 3D information.6.7 To the best property by comparing the shapes or specifical feature of our knowledge,very few methods addressed the descriptions of models.Whereas 3D face detection is to automatic detection of 3D face model. find the local part of the given model that resembles or is 3D face detection is the process of judging whether exactly the face part.It is unfeasible to tackle face model the given 3D model is or just contains the face part,and detection from the point of view of model retrieval. The most distinct property of 3D face is the geometric features of primary facial organs.The method proposed *Correspondence to:Y.Guo,National Laboratory for Novel Software Technology,Nanjing University,Nanjing 210093, in Reference [10]is based on curvature analysis of People's Republic of China.E-mail:ywguo@cad.zju.edu.cn salient facial features,however,the efficiency is relatively Copyright 2007 John Wiley Sons,Ltd.COMPUTER ANIMATION AND VIRTUAL WORLDS Comp. Anim. Virtual Worlds 2007; 18: 483–492 Published online 2 July 2007 in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/cav.192 ........................................................................................... Learning-based 3D face detection using geometric context By Yanwen Guo* , Fuyan Zhang, Chunxiao Liu, Hanqiu Sun and Qunsheng Peng .......................................................................... 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. Received: 15 May 2007; Accepted: 15 May 2007 KEY WORDS: 3D face model; face detection; geometric context; AdaBoost learning Introduction For the wide applications in biometric identification, face tracking, and human computer interaction, 2D face detection and recognition from images were intensively explored in the past decades. Many methods have been brought forward so far.1–5 Since 2D image is prone to variations of pose, expression, and illumination, the robust and efficient techniques are still challenging. With the fast development of 3D scanning techniques, 3D model retrieval is becoming convenient. In contrast with 2D image, 3D model normally contains more inherent information for special modalities. People thus attempt to seek the solution using 3D information.6,7 To the best of our knowledge, very few methods addressed the automatic detection of 3D face model. 3D face detection is the process of judging whether the given 3D model is or just contains the face part, and *Correspondence to: Y. Guo, National Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, People’s Republic of China. E-mail: ywguo@cad.zju.edu.cn if the face part exists, locating its position on the model surface. This technologyis verymeaningful. Forinstance, when producing new characters for animation, it is often necessary to search for the available 3D faces and human models in databases or on web as reference to avoid re￾scanning and re-modeling. Furthermore, the automatic detection of 3D face model will also facilitate 3D face recognition,6 biometric identification,7 automatic texture mapping,8 and so on. 3D face detection involves similar issue as model retrieval, which generally refers to searching models similar to the input one from database. Current methods of model retrieval concentrate on matching global property by comparing the shapes or specifical feature descriptions of models.9 Whereas 3D face detection is to find the local part of the given model that resembles or is exactly the face part. It is unfeasible to tackle face model detection from the point of view of model retrieval. The most distinct property of 3D face is the geometric features of primary facial organs. The method proposed in Reference [10] is based on curvature analysis of salient facial features, however, the efficiency is relatively ............................................................................................ Copyright © 2007 John Wiley & Sons, Ltd