Abstract: In this paper，based on the convolutional neural network Inception-ResNet-v1 model，the training and learning were carried out to realize occluded face recognition. The model method was to embed the image into the Euclidean space of d dimension，and used Triplet Loss as the loss function to directly learn the separability among features. The experiments were conducted with the LFW （labeled faces in wild）data set and the face images collected by the camera. The results showed that the recognition rates of the model were 98. 8% in the case of eyes occlusion，98. 6% in the case of mouth occlusion and 96. 9% in the case of eyes and mouth both occlusion. The recognition rate was up to 98. 2% when the occlusion rate was 20%-30%. It can be concluded from the experimental results that the model can obtain better recognition results under certain occlusion conditions.

Key words: convolutional neural network, triplet loss function, machine learning, face recognition