In order to further improve the accuracy of facial expression recognition, a deep convolutional neural network algorithm fusing global and local features (GL-DCNN) is proposed. The algorithm consists of two improved convolutional neural network branches, global branch and local branch, which are used to extract global features and local features respectively. The features of the two branches are weighted and fused, and the fused features are used for classification. Firstly, global features are extracted. The global branch is based on transfer learning, and the improved VGG19 network model is used for feature extraction. Secondly, local features are extracted. In the local branch, central symmetric local binary pattern (CSLBP) algorithm is used for the first feature extraction, and the local texture information of the original image is obtained, which is input into shallow convolutional neural network for the second feature extraction, so that the local features related to facial expressions are automatically extracted. Thirdly, two cascaded fully connected layers are used to reduce the dimension of the features of the two branches, and different weights are assigned to them for weighted fusion. Finally, softmax classifier is used for classification. The experiment is validated on CK+ and JAFFE datasets, and the classification accuracy is over 95% and 93%, respectively. Compared with other five algorithms, this algorithm has a good overall performance, good recognition effect and good robustness, which can provide an effective basis for facial expression recognition.

Aiming at the problem that the feature vector extracted by original Gabor transform has a high dimension and DBN ignores local information when completing face recognition, a DBN face recognition algorithm based on GCSLBP is proposed. Firstly, the algorithm improves the original Gabor transform, and optimizes it by introducing the LBP . Then the algorithm uses the histogram method to represent the final feature vector, which extracts the rich local features of the image and reduces the dimensionality of the feature vector. Finally, DBN is used to improve the robustness of classification and complete the classification and recognition of faces. The algorithm has been simulated experiments on ORL and CMU_PIE data sets. The experimental results show that the algorithm in this paper effectively improves the face recognition rate and are also robust under light and other transformations.

In order to solve the problem that the face image is not rich in features extracted under complex lighting environments，which leads to a low recognition rate，a local pattern feature extraction and recognition algorithm based on sparse representation is proposed. Firstly，the image is divided into several sub-images and the Dynamic Threshold Central-symmetric Local Binary Pattern ( DTCLBP) algorithm is used to extract features by thresholding the pixels of each sub-block and encoding the results of comparison with the central pixel values into the Central Symmetric Local Binary Pattern ( CSLBP) ; and then second-order features are extracted from the processed image by the former step using the Central Symmetric Local Derivative Pattern ( CSLDP) ; finally，the sparse representation classification algorithm is used to classify and identify the extracted features. The simulation experiments on Extended Yale B，CMU _PIE and AＲ face databases validate the effectiveness of the DTCLBP-CSLDP-SＲC.