Robust identification of users by convolutional neural network in MATLAB and Raspberry Pi

Paula Useche Murillo, Robinson Jiménez-Moreno, Javier Eduardo Martinez Baquero


The following article presents the development of an algorithm embedded in a Raspberry Pi 3B board, where a user identification was made, using the convolutional neural network (CNN) for 5 predefined users, with the option of loading remotely a new network for a new user. Comparatively, the same application was programmed in MATLAB programming software to evaluate the results and identify the advantages between them. Networks were trained for 5 different users, using the Caffe library on the Raspberry Pi, and the MATLAB neural network package on the computer. Where it was found that the training made by Caffe on an embedded system is much slower and less efficient than the ones performed in MATLAB, obtaining less than 55% accuracy with Caffe networks and more than 90% with MATLAB networks, training with the same number of samples, the same architecture, and the same database. Finally, the accuracy obtained through confusion matrix is over 88% in each case of users identification.


Convolutional network; Directed acyclic graph-convolutional neural network; Face recognition; MATLAB; Raspberry pi

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578

This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).