Machine learning models in people detection and identification

Abstract

Introduction: This article is the result of research entitled "Development of a prototype to optimize access conditions to the SENA-Pescadero using artificial intelligence and open-source tools", developed at the Servicio Nacional de Aprendizaje in 2020. Problem: How to identify Machine Learning Techniques applied to computer vision processes through a literature review? Objective: Determine the application, as well as advantages and disadvantages of machine learning techniques focused on the detection and identification of people. Methodology: Systematic literature review in 4 high-impact bibliographic and scientific databases, using search filters and information selection criteria. Results: Machine Learning techniques defined as Principal Component Analysis, Weak Label Regularized Local Coordinate Coding, Support Vector Machines, Haar Cascade Classifiers and EigenFaces and FisherFaces, as well as their applicability in detection and identification processes. Conclusion: The research led to the identification of the main computational intelligence techniques based on machine learning, applied to the detection and identification of people. Their influence was shown in several application cases, but most of them were focused on the implementation and optimization of access control systems, or tasks in which the identification of people was required for the execution of processes. Originality: Through this research, we studied and defined the main machine learning techniques currently used for the detection and identification of people. Limitations: The systematic review is limited to information available in the 4 databases consulted, and the amount of information is variable as articles are deposited in the databases.

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“…Summary of findings. Identify the journals and databases where products related to the authors of our theoretical framework are published more often, d) Identify the frequency of publication of each interdisciplinary area of study and each database, e) Identify our theoretical framework to find synonyms of the core concepts of our research, f) Build our final and comprehensive search equation, g) Use our search equation to systematically identify the state-of-the-art publications about our core concepts, and h) Identify relevant recent scholarship in journals where our core concepts are more frequently published, i) Summarise our findings in a narrative manner [77], classifying our results [78], and identify further research opportunities…”