Aiming at the problem that the traditional local binary pattern（ LBP） feature extraction method does not perform well under the condition of changing of light and facial expression

and the feature extracted by a single method cannot express the feature information of the entire face from multiple angles

an approach of face recognition based on block local binary pattern（ LBP） fusion feature and the support vector machine（ SVM） is proposed. For each face image

it is divided into several blocks

and the LBP features are extracted from each block

then pixel means in different blocks are fused with LBP features. The fused feature vector by all the blocks including LBP features and average pixel values are used to represent the whole face. Finally

the SVM is introduced and used as classifier to classify the above features. Experiments are carried out on YALE

ORL and self-built face database. It turns out that the recognition accuracy can respectively reach 95. 15 %

99. 75 % and96. 25 %. Comparative experiments show that this method is superior to the traditional methods such as C4. 5 decision tree and random forest.