To overcome the deficiency of local gradient coding

which only extracts texture feature in neighborhoods of a fixed size

we propose a novel multi-scale local gradient coding fusion method based on asymmetric regions for feature extraction of facial expressions. A normalized face image is preprocessed by using a Gaussian filter to reduce the impact of noise. Then

the preprocessed expression image is divided into several blocks. For each pixel of each sub-block image

multiple and differently sized operators of local gradient coding based on asymmetric regions are used to obtain two binary sequences. These binary sequences are fused into a new binary sequence according to the logical XOR. The new binary sequence is then encoded

each sub-block histogram distribution is statistically analyzed

and all the sub-block histograms are cascaded into the texture features of a facial expression. Finally

the process of expression classification is completed with the SVM method. Experiments using the proposed method are performed using the JAFFE database and CK database. The average recognition rate for JAFFE is 95.24%

whereas that for CK is 96.83%. The proposed method is compared with LBP

CBP

LGC

and AR-LBP. Experimental results demonstrate that the proposed approach for the JAFFE database achieves recognition rates that are 5.6%

4.85%

3.71%

and 2.40% higher than those achieved with CBP

LBP

LGC

and AR-LBP

respectively. As for the CK database

the proposed approach achieves recognition rates that are 3.66%

2.50%

2.17%

and 1.66% higher than those achieved with CBP

LBP

LGC

and AR-LBP

respectively. Cross validation results show that the proposed method for facial expression recognition has excellent accuracy. Through the fusion of the intensities between neighborhoods of different gradients and scales

the multi-scale local gradient coding fusion method based on asymmetric regions combined with block histograms can perform well in local and global feature description. Experiment results show that the proposed method is better than typical feature extraction algorithms and is suitable for static facial expression recognition.