Journal of Jilin University(Engineering and Technology Edition) ›› 2019, Vol. 49 ›› Issue (5): 1715-1725.doi: 10.13229/j.cnki.jdxbgxb20180627

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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

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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

CLC Number: 

  • TN911.7

Fig.1

Calculating process of shear wave coefficients and translation combination of scaling functions on anisotropic meshes"

Fig.2

Processing diagram of DST separability assessment system"

Fig.3

Image Lena and energy peak varies with decomposition scale in shearlet domain"

Fig.4

Energy ratio changes with decomposition scale in shearlet domain"

Fig.5

Reconstructed image of shearlet low and high-frequency coefficients"

Fig.6

Samples face expression images from JAFFE and CK+ datasets"

Fig.7

Normalization and equalization of facial images"

Table 1

Expression recognition rate of SVM using"

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

Fig.8

Effect of two parameters on recognition rate"

Fig.9

Separability indexing of high-frequency coefficients in each direction of two datasets"

Fig.10

Change of recognition rate and running time with number of directions on two datasets"

Table 2

Comparison of proposed facial expression"

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

Table 3

Comparison of proposed facial expression"

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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