Date of Award
1-1-2022
Document Type
Thesis
Degree Name
M.S. in Engineering Science
Department
Engineering Science
First Advisor
Yixin Chen
Second Advisor
Dawn Wilkins
Third Advisor
Timothy Holston
Relational Format
dissertation/thesis
Abstract
The current facial recognition algorithms struggle with accuracy on real world cases. Haar cascade based algorithms are fast, but require fine tuning per image in order to achieve the best results. When tasked with images where there are multiple faces at different locations, the current algorithms seem to underreport the number of faces. This study attempts to produce a more accurate classifier through the use of taking the maximum result of multiple Haar cascade classifiers with differing parameters. To do this, a web image scraper was written to gather real world images from Google images and Flickr. These images were analyzed using the OpenCV library utilizing multiple Haar cascade classifiers and the maximum of these classifiers was taken. The result is a more accurate classifier, as most of the inaccuracies were due to undercounting, rather than overcounting.
Recommended Citation
Zhao, Chris, "Face Detection Using Web Images" (2022). Electronic Theses and Dissertations. 2300.
https://egrove.olemiss.edu/etd/2300
Concentration/Emphasis
Computer Science