The purpose of this article was to provide a practical approach to the application of the Toolbox Deep Learning, from the Matlab software, for facial recognition of a group of students from the Mechatronics Engineering Program, at the Ricardo Palma University, Lima-Peru. To do this, the methodology used consisted of defining the architecture, configuration and training of a deep learning convolutional neural network, to extract the relevant data of facial features in the photographs taken of the group of students. The sample used was 426 photographs corresponding to 14 students; Likewise, different tests were carried out to select the most appropriate number of layers, the highest percentage of precision and the shortest training time. And, the best result corresponded to the use of two convolution layers, of 16 and 32 filters, respectively, with a precision percentage of 94.00% in the facial recognition of the group of students.