期刊文献+

基于层次分析法语义知识的人脸表情识别新方法 被引量:17

A novel facial expression recognition method based on semantic knowledge of analytical hierarchy process
原文传递
导出
摘要 在目前的人脸表情识别系统中,人脸表情的机器识别和人类感知之间存在着本质的差异,造成人脸表情识别率不高。为了减小人脸图像底层视觉特征与高层语义之间的语义鸿沟,提出一种基于层次分析法(AHP)语义知识的人脸表情识别新方法。该方法首先采用层次分析法对训练集中人脸图像进行高层语义描述,建立语义特征向量,在底层视觉特征提取阶段,提出一种二阶PCA(principal component analysis)方法来提取人脸图像的纹理特征;在识别阶段,仅利用输入人脸图像的底层视觉特征,采用K-NN(k-nearest neighbor)算法并结合学习阶段建立的语义特征向量,进行人脸表情分类识别。提出的人脸表情识别方法结合底层视觉特征和高层语义知识,减小了人脸图像底层视觉特征与高层语义之间的语义鸿沟。在JAFFE人脸表情数据库中进行实验,获得了93.92%的平均识别率。理论分析和实验结果表明,与其他的人脸表情识别方法相比,该方法具有更好的识别效果。 At present,there are intrinsic differences between machine recognition of facial expression and human perception in the facial expression recognition system, which affect the precision of facial expression recognition. In order to reduce the semantic gap between the low-level visual features of face images and high-level semantic, a novel facial expression recognition method based on semantic knowledge of analytical hierarchy process (AHP) is presented. The analytical hierarchy process method is adopted to describe the high-level semantic of face images of the training set, which further used to establish semantic features. In the stage of low-level visual features extraction, the 2rid-order principal component analysis method is proposed to extract the texture features of face images. In the recognition stage, only low-level visual features of the input face image is used, and k-nearest neighbor method combined with semantic features in the study stage is used to classify the facial expressions. The proposed method combines the low-level visual features with high-level semantic features, reducing the semantic gap between them. The experiments are conducted on Japanese Female Facial Expression (JAFFE) database and the overall recognition rate of 93.92% is achieved. Theoretical analysis and experimental results both show that the proposed method has higher recognition rate.
出处 《中国图象图形学报》 CSCD 北大核心 2011年第3期420-426,共7页 Journal of Image and Graphics
关键词 人脸表情识别 层次分析法 底层视觉特征 高层语义 facial expression recognition analytical hierarchy process low-level visual feature high-level semanteme
  • 相关文献

参考文献10

  • 1Kim M, Lee H S, Jeong W P, et al. Determining color and blinking to support facial expression of a robot for conveying emotional intensity [ C ]// Proceedings of the 17th International Symposium on Robot and Human Interactive Communication. New Jersey: IEEE Computer Society, 2008: 219-224.
  • 2Lee K K, Xu Y. Real-time estimation of facial expression intensity [ C ]// Proceedings of the 2003 IEEE International Conference on Robotics and Automation. New York: IEEE lnc 2003 : 2567-2572.
  • 3Shishir Bashyal, Ganesh K Venayagamoorthy. Recognition of facial expressions using Gabor wavelets and learning vector quantization [ J ]. Engineering Applications of Artificial Intelligence, 2008,21 (7) : 1056-1064.
  • 4Geetha A, Ramalingam V, Palanivel S, et al. Facial expression recognition - a real time approach [ J ]. Expert Systems with Applications, 2009,36 (1) :303-308.
  • 5Abu Sayeed Md Sohail, Prabir Bhattacharya. Glassifying facial expressions using point-based analytic face model and Support Vector Machines [ C ]//Proceedings of the 2007 IEEE International Conference on Systems Man and Cybernetics. New York: IEEE Inc., 2007:1008-1013.
  • 6Zhang Yongmian, Ji Qiang, Zhu Zhiwei. Facial expression analysis and synthesis with MPEG-4 facial animation parameters [ J ]. IEEE Transactions on Circuits and Systems for Video Technology, 2008,18(10) :1383-1395.
  • 7Aleksic P S, Katsaggelos A K. Automatic facial expression recognition using facial animation parameters and multistream HMMs[ J ]. IEEE Transactions on Information Forensics and Security, 2006,1 ( 1 ) :3-11.
  • 8Cheng Shyichyi, Chen Mingyao, Chou T C, et al. Semanticbased facial expression recognition using analytical hierarchy process[J]. Expert Systems with Applications,2007,33 (1) : 86-95.
  • 9Kima Hyunchul, Daijin Kima, Sung Yang Banga, et al. Face recognition using the second-order mixture-of-eigenfaces method [J]. Pattern Recognition, 2004,37(2) :337-349.
  • 10Shu Liao, Wei Fan, Albert C S, et al. Facial expression recognition based on local binary, patterns and coarse-to-fine classification[ C ]// Proceedings of the 2006 IEEE International Conference on Image Processing. New Jersey: IEEE Computer Society, 2006:665-668.

同被引文献122

  • 1赵国杰,邢小强.ANP法评价区域科技实力的理论与实证分析[J].系统工程理论与实践,2004,24(5):41-45. 被引量:82
  • 2左坤隆,刘文耀.基于活动外观模型的人脸表情分析与识别[J].光电子.激光,2004,15(7):853-857. 被引量:19
  • 3赵磊,杨路明,吴建辉.指纹图像预处理新方法[J].计算机应用,2007,27(4):929-931. 被引量:10
  • 4Ekman P. Emotion in the Human Face. New York: Cam- bridge University Press, 1982.
  • 5Lin Hong, Wan Yi - fei, Anil Jain. Finger print image en- hancement algorithm and performance evaluation [J]. IEEE Transactions on Patten analysis and Machine intelligence 1998, 20(8) :777 -789.
  • 6Chang Y, Hu CB, Turk M. Probabilistic expression analysis on manifolds. In: Proc. of the IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR 2004), Vol. 2. 2004. 520 - 527.
  • 7Elgammal A, Lee CS. Separating style and content on a nonlinear manifold. In : Proc. of the IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR 2004), Vol. 1. 2004. 478 - 485.
  • 8Shan CF, Gong SG, McOwan PW. Dynamic facial expres- sion recognition using a Bayesian temporal manifold model. In: Proc. of the British Machine Vision Conf. (BMVC 2006), Vol. 1. 2006. 297 -306.
  • 9YIN L J,WEI X ZH,SUN Y,et al. A 3D facia expression database for facial behavior research [ C ]. IEEE 7th International Conference on Automatic Face and Gesture Recognition (FGR06), Southampton, UK, 10-12, 2006: 211-216.
  • 10TANG H, HUANG T S. 3D facial expression recognition based on automatically selected features [ C ]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops ,2008,6 : 1-8.

引证文献17

二级引证文献58

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部