吉林大学学报(工学版) ›› 2019, Vol. 49 ›› Issue (5): 1715-1725.doi: 10.13229/j.cnki.jdxbgxb20180627

• • 上一篇    

基于离散Shearlet类别可分性测度的人脸表情识别方法

卢洋(),王世刚(),赵文婷,赵岩   

  1. 吉林大学 通信工程学院,长春 130022
  • 收稿日期:2018-06-19 出版日期:2019-09-01 发布日期:2019-09-11
  • 通讯作者: 王世刚 E-mail:407851137@qq.com;wangshigang@vip.sina.com
  • 作者简介:卢洋(1991-),女,博士研究生.研究方向:数字图像处理.E-mail:407851137@qq.com
  • 基金资助:
    国家自然科学基金重点项目(61631009);国家“十三五”重点研发计划项目(2017YFB0404800)

Facial expression recognition based on separability assessment of discrete Shearlet transform

Yang LU(),Shi-gang WANG(),Wen-ting ZHAO,Yan ZHAO   

  1. Colloge of Communication Engineering, Jilin University, Changchun 130022, China
  • Received:2018-06-19 Online:2019-09-01 Published:2019-09-11
  • Contact: Shi-gang WANG E-mail:407851137@qq.com;wangshigang@vip.sina.com

摘要:

针对优化表情特征稀疏表达问题,提出一种基于离散Shearlet类别可分性测度的人脸表情识别方法。首先,对预处理后的图像进行离散Shearlet变换,得到变换系数。然后,根据测度函数评估每个方向与尺度系数的可分性指标,在最佳可分性方向与尺度上,融合低频和高频系数作为特征。最后,引入支持向量机进行分类。结果证明:本文方法选取具有最佳可分性的尺度和方向系数作为特征,抛弃了无用信息,降低了特征维度与计算量,使系统更高效。

关键词: 信息处理技术, 离散可分离剪切波变换, 人脸表情识别, 可分性评价, 多尺度几何分析, 支持向量机

Abstract:

To solve the problem of sparse expression of facial expression features, a facial expression recognition method based on separability assessment of the Discrete Shearlet Transform (DST) is proposed. DST is a relatively new image multiscale geometric analysis method. First, the DST transform is applied to the preprocessed facial expression images, and the transformation coefficients are obtained. Then, according to the separability evaluation function, the separability index in each direction and the scale coefficients are evaluated, and the low and high-frequency coefficients are fused on the best separability direction and scale. Finally, Support Vector Machine (SVM) is introduced to classify the facial expression. The experimental results show that the proposed method can select the best separability scale and direction coefficient as the feature, abandon the useless information, reduce the feature dimension and computation cost, therefore, the system is more efficient.

Key words: information processing technology, discrete separable shearlet transform, facial expression recognition, separability assessment, multiscale geometric analysis, support vector machine

中图分类号: 

  • TN911.7

图1

剪切波系数计算过程及尺度函数在各向异性网格上的平移组合"

图2

离散可分性评价系统"

图3

Lena图像和峰值随分解尺度的变化曲线"

图4

能量比随 j 变化曲线"

图5

各尺度下低频与高频系数重构图像"

图6

表情库样本图像"

图7

图像预处理"

表1

基于不同核函数的SVM表情识别率"

核函数 识别率/%
JAFFE CK+
线性 95.86 92.05
二阶多项式 96.71 92.89
三阶多项式 97.18 94.17
RBF 97.65 94.60
Sigmoid 82.63 87.50

图8

两个参数对识别率的影响"

图9

两个表情库高频系数可分性指标"

图10

两表情库识别率与运行时间随方向数目的变化"

表2

本文方法与目前方法用于JAFFE表情库的识别率"

算法 特征 识别率/%
本文 SST-SJ+SVM 96.26
文献[8] DST+SVM 89.01
文献[16] 局部曲波变换 94.65
文献[17] Radial encoded Gabor jets 89.67
文献[18] Patch-based-Gabor 92.93
文献[19] FEETS+PRNN 83.84
文献[20] Overlap LBP+FA variant+Ensemble(SVM) 87.75

表3

本文方法与目前方法用于CK+表情库的识别率"

算法 特征 识别率/%
本文 SST-SJ+SVM 94.05
文献[21] CSPL 89.89
文献[22] 基于TC特征 88.90
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