人脸表情识别研究
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摘要
人脸表情识别(Face Expression Recognition,FER)是计算机自动人脸表情识别的简称,指利用计算机技术对人脸表情信息进行特征提取,按照人的认识和思维方式加以归类和理解,进而从人脸信息中去分析理解人的情绪,是计算机视觉研究的重要组成部分。
表情识别的核心在于表情特征的提取。计算机自动识别人脸表情之所以困难,原因在于人脸是一个柔性体,为人脸表情特征建立精确数学模型的难度较高。脸部器官的位置稍有变动,表情就会发生巨大的变化,因而如何选择特征成为决定识别精度的关键。本文着眼于特征提取,从单特征提取和多特征融合两个方面对人脸表情识别做了比较深入的分析与研究。
类内PCA是对每一类训练样本分别进行PCA处理,使得类间样本距离增大,类内样本距离缩小。论文将类内PCA方法应用于表情识别,并将这种方法与类间PCA方法进行了比较。
每种表情特征在识别上都有其自身的局限性。因此,仅仅提取某一种特征不会得到很高的识别率。基于此,本文给出了一种新的表情特征——角度变化几何特征,并运用特征融合技术对人脸表情的Gabor特征和角度变化几何特征进行了融合。
实验证明,类内PCA方法的识别精度高于类间PCA,而角度变化几何特征和经过类内PCA提取的Gabor特征相融合后的识别率又高于类间PCA和类内PCA。
Facial Expression Recognition (FER), which is the short title of the Automatic Facial Expression Recognition, refers to the process that people use computer technology to extract the feature information of Human Facial Expression, and class the information in accordance with understanding and ways of thinking of human, and analyze the emotion. FER is an important part of the Computer Vision Research.
Feature Extraction is the core of Facial Expression Recognition. Automatic Facial Expression Recognition is difficult, because the face is a deformable body and difficult to build a precise mathematical model. The expression will change greatly when the place of organs in the face changes lightly. Therefore, the vital factor of deciding the recognition accuracy is how to select the features from human faces. Focusing on Feature Extraction, we analyze and research the Human Facial Expression Recognition deep in two ways of the Single-feature Extraction and the Multi-feature Fusion.
Within-Class PCA is the process, in which each type of training samples are processed in PCA, the distances of the within-class samples are made much narrow while the distance of inter-class samples are made much large. In this paper, we use the within-class PCA method for Facial Expression Recognition and compare the approach with inter-class PCA.
To Expression Recognition, each type of Facial Expression Feature has its own limitations. As a result, only extracting a kind of feature won't win well recognition rate. For this reason, the paper presents a new expression feature that is Angle Change Geometry Feature, and fuses the Gabor Feature and Angle Change Geometry Feature.
Experiments have proved that the recognition accuracy of within-class PCA is higher than inter-class PCA, while the recognition accuracy of the fusion is higher than inter-class PCA and within-class PCA.
表情识别的核心在于表情特征的提取。计算机自动识别人脸表情之所以困难,原因在于人脸是一个柔性体,为人脸表情特征建立精确数学模型的难度较高。脸部器官的位置稍有变动,表情就会发生巨大的变化,因而如何选择特征成为决定识别精度的关键。本文着眼于特征提取,从单特征提取和多特征融合两个方面对人脸表情识别做了比较深入的分析与研究。
类内PCA是对每一类训练样本分别进行PCA处理,使得类间样本距离增大,类内样本距离缩小。论文将类内PCA方法应用于表情识别,并将这种方法与类间PCA方法进行了比较。
每种表情特征在识别上都有其自身的局限性。因此,仅仅提取某一种特征不会得到很高的识别率。基于此,本文给出了一种新的表情特征——角度变化几何特征,并运用特征融合技术对人脸表情的Gabor特征和角度变化几何特征进行了融合。
实验证明,类内PCA方法的识别精度高于类间PCA,而角度变化几何特征和经过类内PCA提取的Gabor特征相融合后的识别率又高于类间PCA和类内PCA。
Facial Expression Recognition (FER), which is the short title of the Automatic Facial Expression Recognition, refers to the process that people use computer technology to extract the feature information of Human Facial Expression, and class the information in accordance with understanding and ways of thinking of human, and analyze the emotion. FER is an important part of the Computer Vision Research.
Feature Extraction is the core of Facial Expression Recognition. Automatic Facial Expression Recognition is difficult, because the face is a deformable body and difficult to build a precise mathematical model. The expression will change greatly when the place of organs in the face changes lightly. Therefore, the vital factor of deciding the recognition accuracy is how to select the features from human faces. Focusing on Feature Extraction, we analyze and research the Human Facial Expression Recognition deep in two ways of the Single-feature Extraction and the Multi-feature Fusion.
Within-Class PCA is the process, in which each type of training samples are processed in PCA, the distances of the within-class samples are made much narrow while the distance of inter-class samples are made much large. In this paper, we use the within-class PCA method for Facial Expression Recognition and compare the approach with inter-class PCA.
To Expression Recognition, each type of Facial Expression Feature has its own limitations. As a result, only extracting a kind of feature won't win well recognition rate. For this reason, the paper presents a new expression feature that is Angle Change Geometry Feature, and fuses the Gabor Feature and Angle Change Geometry Feature.
Experiments have proved that the recognition accuracy of within-class PCA is higher than inter-class PCA, while the recognition accuracy of the fusion is higher than inter-class PCA and within-class PCA.
引文
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[9]Haisong Gu,Qiang Ji.An Automated Face Reader for Fatigue Detection.In:The 6th International Conference on Automatic Face and Gesture Recognition.Seoul,Korea:2004,111-116.
[10]刘晓曼,谭春华,章毓晋.人脸表情识别研究的新进展[J].中国图形图像学报,2006:10(11):1359-1366.
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[12]何良华,邹采荣,包永强,赵力.人脸面部表情识别的研究进展[J].电路与系统学报,2005-2,10(1):70-73.
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[19]Lavagetto Fabio,Pockaj Roberto.An Efficient Use Of MPGE-4 FAP Interpolation for Facial Animation at 70 bits/Frame[J].IEEE Transactions.on circuits and systems for video technology,2001-10,11(10).
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[23]Baek Kyungim,Draper Bruce A,Beveridge J Ross,She Kai.PCA vs.ICA:A comparison on the [OL].http://www.dodcounterdrug.com/facialrecognition/FRVT2000/frvt2000.htm.
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[25]张林等.基于主元分析和Fuzay ART模型的人脸识别算法[J].电路与系统学报,1999-09,4(3).
[26]赵力庄等.Eigenface的变维分类方法及其在表情识别中的应用[J].计算机学报,1999-06,22(8).
[27]周杰,卢春雨,张长水,李衍达.人脸自动识别方法综述[J].电子学报,2000-04,28(4).
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[31]Ding Peilv,Kang Xuelei,Zhang Liming.Personal Recognition Using ICA[A].ICONIP2001Proceedings[C].2001,3:1179-1184.
[32]Havran C,et al.Independent Component Analysis for face authentication[A].KES'2002 proceedingsknowledge-based intelligent Information and Engineering Systems[C].Crema(Italy),2002-09.1207-1211.
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