Using face detection to secure places is an important application merging with machine vision. This paper reposed a system to do face detection and recognition using existing architecture google-net and transfer learning to let the network learn images based on pre-trained architecture, The design of a network leads to an architecture that leads to maximizing the accuracy of the system and accurately detecting faces that are saved to the database and specifying the effect of weights used within the nodes of the hidden layer which consider the most time-consuming task within the architecture. The main characteristics are explained and the architecture used as well as the used data set in detail with the design of a network which provides an architecture that leads to maximizing the accuracy of the system and accurately detecting faces that are saved to the database and specifying the effect of weights used within the nodes of the hidden layer which consider the most time-consuming task within the architecture. Experimental results show using epochs 10 and 100 samples imply 98.37% training accuracy whereas other numbers of epochs either provide less accuracy or consume more time, and the number of epochs and training samples can be modified according to the system requirements. Other factors like illumination, the color of the background, and face rotation or scale were discussed as impact factors.