Authors :
Tamara Regina; Christopher Matthew
Volume/Issue :
Volume 8 - 2023, Issue 6 - June
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/yzrwek3y
DOI :
https://doi.org/10.5281/zenodo.8126042
Abstract :
The development of effective surveillance and
security systems is crucial for public safety and for
preventing criminal activities. However, lack of proper
infrastructure and technological advancements has
hindered the progress of surveillance and security
systems in many parts of the world. One significant
challenge in this regard is the difficulty in suspect
identification from manually inspecting several footags.
In this paper, we propose the optimization of facial
recognition systems, commonly used in surveillance and
safety, through several image processing techniques such
as Eigen Faces, Fisher Faces, and Facial Coordinates
based on the minimum cross entropy theory. Our
approach involves several features incorporating facial
recognition that can be analyzed to identify potential
suspects. By the end of this paper, we found an
interesting result that only one can accurately predict
facial recognition from video footage given the training
image.
The development of effective surveillance and
security systems is crucial for public safety and for
preventing criminal activities. However, lack of proper
infrastructure and technological advancements has
hindered the progress of surveillance and security
systems in many parts of the world. One significant
challenge in this regard is the difficulty in suspect
identification from manually inspecting several footags.
In this paper, we propose the optimization of facial
recognition systems, commonly used in surveillance and
safety, through several image processing techniques such
as Eigen Faces, Fisher Faces, and Facial Coordinates
based on the minimum cross entropy theory. Our
approach involves several features incorporating facial
recognition that can be analyzed to identify potential
suspects. By the end of this paper, we found an
interesting result that only one can accurately predict
facial recognition from video footage given the training
image.
