Access Control with Facial Recognition: A Systematic Literature Review

Resumo

Counting people or even identifying individuals present in a given place is a fundamental task for public and private sectors and/or institutions that need to manage the access and permanence of individuals in certain places. This is not different at the Federal Institute of Education, Science and Technology of Mato Grosso do Sul - Campus Três Lagoas, which has a large physical space and only a few employees to control and supervise these facilities, which are shared by high school and college students, requiring more strict access control, especially in research laboratories. Several tools and methodologies have been proposed to meet this demand, including the use of DeepLearning, convolutional networks and a wide variety of facial recognition algorithms, which show great potential, as they are present in several solutions in the literature. The aim of this paper is to carry out a systematic review of the literature, broadly investigating best practices and success cases in using facial recognition to control people's access to environments. The review makes a significant contribution to those who intend to develop or research in this field, as it presents an overview and discusses what has already been tested and returned a good outcome and what did not generate a satisfactory result, as well as presenting the results of tests and proofs of concept carried out at the IFMS - Campus Três Lagoas.