Feature extraction is the most critical step in pattern recognition

and facial expression recognition is no exception.Weber Local Descriptor (WLD) is a Method that can effectively extract texture information from images and has the advantages of being consistent with human perception of human beings and being insensitive to noise and non-monotonic illumination variations. However

WLD has some limitations in the feature representation of local details. To overcome these limitations

a facial expression recognition Method based on Pyramid WLD (PWLD) is proposed in this study. First

facial images are preprocessed. This step includes the detection of faces from facial expression databases and normalization. The salient regions of segment 2 that have significant contributions to facial expression recognition from images are also preprocessed. One of these salient regions is that which includes the eyes and eye brows

while another is that with the mouth. The sizes of salient regions differ

and these regions contain different information. Thus

we stratify these salient regions and divide each layer into different blocks. The PWLD features of each block in each layer are then extracted and cascaded to represent the global and local features of a salient region reasonably

with some parameter adjustments. Second

we compute for the Chi-square distance of the PWLD histograms in both the testing and training sets. We then choose the minimum distance in every category of expressionsand normalize this distance to construct the Basic Probability Assignment (BPA) as independent evidence. To create the BPA

we use curve fitting in numerical analysis by simulating several sets of data. Finally

fusion BPA is obtained by using the Dempster-Shafer rule

and the Results are further obtained by employing thedecision-making and judgment of Dempster-Shafertheory of evidence. By fusing the PWLD features of the two different salient regions with Dempster-Shafer theory of evidence

we can overcome the limitations of a single regional featureand acquire more reliable and accurate Results. We conduct some cross-validation experiments on the JAFFE and Cohn-Kanade facialexpression databases

and the average recognition rates reach up to 95.81% and 97.47%

respectively. In addition

we perform some experiments with other algorithms

such as LBP

LDP

and Gabor; we also conduct some comparative experiments that combine the PWLD with different classifiers

such as 1-NN and SVM. The WLD

which is known as a robust image descriptor

can well extract the texture information of images. Moreover

the PWLD can accurately describe the local details

which have more advantages than the WLD features. The comparative Results of some typical Methodsverify the effectiveness and fault tolerance of the proposed Method. The proposed Method has certain robustness under simultaneous noise and light conditions.