Most existing face recognition algorithms adopt various improved convolutional neural network（ CNN）algorithms in Deep Learning. But the algorithm has many problems

such as many parameters and long training time. Therefore

in order to reduce the time consumed in the training process and the amount of calculation in the classification process

an improved MobileNet algorithm is designed to realize face recognition

and transplants it to Jetson nano equipment to form a complete outdoor security system. The original Soft Max classifier in MobileNet model is improved. Through comparative experiments

it is found that the effect of using A-SoftMax classifier is better than that of Soft Max. The experimental results show that the proposed model achieves 97. 4 % accuracy on LFW face database

reduces computing time to 1/9 of traditional CNN

and reduces computing parameters to 1/7 of traditional CNN.