The Face Detection / Recognition , Perspective and Obstacles In Robotic: A Review

Nafiz Md Imtiaz Uddin; Ata Jahangir Moshayedi; Hong lan1; Yang Shuxin

doi:10.4108/airo.v1i1.2836

Authors

Nafiz Md Imtiaz Uddin Jiangxi University of Science and Technology
Ata Jahangir Moshayedi Jiangxi University of Science and Technology https://orcid.org/0000-0002-9457-6267
Hong lan1 Jiangxi University of Science and Technology
Yang Shuxin Jiangxi University of Science and Technology

DOI:

https://doi.org/10.4108/airo.v1i1.2836

Keywords:

Face Detection and Recognition, Computer Vision, Detection Tracking, Human Robot, Service robot

Abstract

Facial recognition research is one of the different types of research in this world today. In recent years, facial recognition in robots has attracted increased study interest. Robotic platforms now utilize a variety of object detection methods, with face detection being a viable use. Face detection in robotics is a computer technique that recognizes human faces in digital pictures and is used in a range of applications. Different authors have performed their research in different ways on the use of detection systems. This paper aims to give future researchers a better idea of using facial recognition systems in robotics. In this study, we reviewed research by various authors over recent years to facilitate future facial recognition research. In addition, scholars have addressed the topics, how they have done so, and the specifics of their approaches are described. This paper reviewed an overview of hardware implementation and software implementation by various authors. It can automatically focus cameras or count the number of people who have entered a location. Commercial applications of the method include displaying tailored advertisements in response to a recognized face along with the algorithms, functions and architectures used in facial recognition and giving the opinions of various authors mentioned. The comparative analysis of facial recognition and its architecture system is highlighted.

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