全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

精确局部特征描述的表情识别

DOI: 10.11834/jig.20141109

Keywords: 表情识别,精确局部特征,充分矢量三角形模式,多种尺度

Full-Text   Cite this paper   Add to My Lib

Abstract:

目的针对传统局部特征提取方法在表情识别上的局限性,提出一种精确局部特征描述的表情识别方法。方法首先将人的眉毛、眼睛和嘴巴3个对表情识别起关键作用的器官分割出来,使得特征描述更具有针对性。然后,构造充分矢量三角形以统计图像的轮廓特征与细节特征。最后,对于不同的表情器官采用不同尺度的充分矢量三角形描述,对于同种表情器官采用多种尺度的充分矢量三角形联合描述,从而充分描述关键器官的图像信息。结果该算法在日本女性表情人脸库(JAFFE)、cohn-kanade库(CK)以及Painexpressions库上进行实验,分别取得了95.67%、97.83%、84.0%的平均识别率以及11.70ms、30.23ms、11.73ms的平均特征提取时间,实验结果表明,精确局部特征描述的表情识别方法可以较快、较准确的进行人脸表情识别。结论精确局部特征描述的表情识别方法通过器官的分割以及充分矢量三角形模式的构造与灵活运用,良好地表达了图像的局部特征且具有较低的时间复杂度,本文算法与目前典型的表情识别算法的实验对比结果也表明了本文算法的有效性。

References

[1]  Liao C T, Chuang H J, Duan C H,et al. Learning spatial weighting for facial expression analysis via constrained quadratic programming[J]. Pattern Recognition, 2013, 46(11): 3103-3116.
[2]  Baltrusaitis T, McDuff D, Banda N,et al. Real-time inference of mental states from facial expressions and upper body gestures[C]//Proceedings of IEEE International Conference on Automatic Face & Gesture Recognition and Workshops. Santa Barbara,CA:IEEE,2011:866-871.
[3]  Gao F, Wen G J. A new method for affine invariants extraction based on affine geometry[J]. Journal of Image and Graphics, 2011, 16(3): 389-397.[高峰,文贡坚. 利用仿射几何的仿射不变特征提取方法[J]. 中国图象图形学报, 2011,16(3):389-397.][DOI:10.11834/jig.20110315]
[4]  Song K C, Yan Y H, Chen W H, et al. Research and perspective on local binary pattern[J]. Acta Automatic Sinica, 2013, 39(6):730-744.[宋克臣,颜云辉,陈文辉,等. 局部二值模式方法研究与展望[J]. 自动化学报, 2013, 39(6):730-744.]
[5]  Xue Y L, Mao X, Guo Y, et al. The research advance of facial expression recognition in human computer interaction[J]. Journal of Image and Graphics, 2009, 14(5): 764-772.[薛雨丽,毛峡,郭叶,等. 人机交互中的人脸表情识别研究进展[J].中国图象图形学报, 2009, 14(5): 764-772.][DOI:10.11834/jig.20090503]
[6]  Wei R, Jiang L, Tao L M. Facial expression recognition system based on multiple feature integration[J]. Journal of Image and Graphics, 2009, 14(5): 792-800.[魏冉,姜莉,陶霖密. 融合人脸多特征信息的表情识别系统[J]. 中国图象图形学报, 2009, 14(5): 792-800.][DOI: 10.11834/jig.20090506]
[7]  Happy S L, George A, Routray A. A real time facial expression classification system using local binary patterns[C]//Proceedings of IEEE International Conference on Intelligent Human Computer Interaction. Kharagpur:IEEE, 2012:1-5.
[8]  Jabid T, Kabir M H, Oksam C. Facial expression recognition using local directional pattern[C]//Proceedings of IEEE International Conference on Image Processing. Hongkong, China: IEEE, 2010:1605-1608.
[9]  Singh S, Maurya R, Mittal A. Application of complete local binary pattern method for facial expression recognition[C]//Proceedings of IEEE International Conference on Intelligent Human Computer Interaction. Kharagpur:IEEE, 2012:1-4.
[10]  Setyati E, Suprapto Y K, Pumono M H. Facial emotional expressions recognition based on active shape model and radial basis function network[C]//Proceedings of IEEE International Conference on Computational Intelligence for Measurement Systems and Application. Tianjin:IEEE,2012:41-46.
[11]  Astana A, Saragih J, Wagner M, et al. Evaluating AAM fitting methods for facial expression recognition[C]//Proceedings of IEEE International Conference on Affective Computing and Intelligent and Workshops. Amsterdam:IEEE,2009:1-8.
[12]  Liu H B, Zhang G B, Huang Y M, et al. Multiple features extraction and coordination using gabor wavelet transformation and fisherfaces with application to facial expression recognition[C]//Proceedings of Chinese Conference on Pattern Recognition. Chongqing:IEEE,2010:1-5.
[13]  Zhao Y, Su J B. Local characteristic pattern make use of vector triangle for face recognition[J]. Electronic Journal, 2012, 40(11):2309-2314.[赵?,苏剑波. 一种用于人脸识别的矢量三角形局部特征模式[J]. 电子学报, 2012,40(11):2309-2314.]
[14]  Dai D W, Liu D, Su J B. Rapid eye localization based on projection Peak[J]. PR&AI, 2009, 22(4):605-609.[戴景文,刘丹,苏剑波.基于投影峰的眼睛快速定位方法[J]. 模式识别与人工智能, 2009, 22(4):605-609.]
[15]  Liu D H, Qian H, Dai G, et al. An iterative SVM approach to feature selection and classification in high-dimensional datasets[J]. Pattern Recognition, 2013, 46(9):2531-2537.
[16]  Patle A, Chou H, Deepak S. SVM kernel functions for classification[C]//Proceedings of IEEE International Conference on Advances in Technology and Engineering. Mumbai:IEEE,2013:1-9.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133