As deep residual network has problems such as complex network structure and high time cost in face recognition applications of small mobile devices， a lightweight model based on deep residual network was proposed. Firstly， by simplifying and optimizing the structure of the deep residual network and combining the knowledge transfer method， a lightweight residual network （student network） was reconstructed from the deep residual network （teacher network）， which reduced the network structural complexity while ensuring accuracy. Then， in the student network， the parameters of the model were reduced by decomposing standard convolution， thereby reducing the time complexity of the feature extraction network. Experimental results show that on four different datasets such as LFW （Labeled Faces in the Wild）， VGG-Face （Visual Geometry Group Face）， AgeDB （Age Database） and CFP-FP （Celebrities in Frontal Profile with Frontal-Profile）， with the recognition accuracy close to the mainstream face recognition methods， the proposed model has the time of reasoning reaches 16 ms every image， and the speed is increased by 10% to 20%. Therefore， the proposed model can have the speed of reasoning effectively improved with the recognition accuracy basically not reduced.