To reduce the effect of partial occlusion in facial expression recognition

this paper proposes a new method of facial expression recognition based on local feature fusion. First

the normalized images are processed by the Gaussian filter to reduce the effect of noise. According to their different contributions in facial expression recognition

all the images are then divided into two main parts: near the eye and near the mouth. To analyze considerable structure detail

these two parts are further divided into several non-overlapping blocks. The following two patterns are used to extract the features of each sub block: the difference center-symmetric local binary pattern

which is the change of center-symmetric local binary pattern; and the gradient center-symmetric local directional pattern

which is the change of difference local directional pattern. The features are marked as two binary sequences

which are then cascaded to obtain the characteristic histogram of the sub block. The final histogram of the image is obtained by cascading the histogram of each sub block. Finally

the nearest neighbor method is used for classification. Chi-square distance is used to calculate the distance among the characteristic histograms of the testing and training images. Considering the difference of the amount of information contained in each sub block and to reduce the effect of occlusion further

information entropy is used to weigh chi-square distance adaptively. Three cross experiments are conducted on JAFFE and CK databases. The average recognition accuracies in random occlusion

mouth occlusion

and eye occlusion cases are 92.86%

94.76%

and 86.19% on JAFFE database

and are 99%

98.67%

and 99% on CK database. In the aspect of feature extraction

our method describes the image from two aspects: one is the difference of the pixel values in the gradient direction

and the other is the difference of the edge response values between gradient directions. Accordingly

the image can be fully described. In the aspect of occlusion

image segmentation and information entropy are used to weigh chi-square distance adaptively. Thus

our method can effectively reduce the effect of occlusion. Under the same experimental conditions

experimental results show the effectiveness and superiority of the proposed method to other classical local feature extraction and occlusion handling methods.