國立虎尾科技大學 |

Face Detection Using Web Images.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Face Detection Using Web Images./
作者:	Zhao, Chris.
面頁冊數:	1 online resource (27 pages)
附註:	Source: Masters Abstracts International, Volume: 84-01.
Contained By:	Masters Abstracts International84-01.
標題:	Information science. -
電子資源:	click for full text (PQDT)
ISBN:	9798834044734

Face Detection Using Web Images.
Zhao, Chris.

Face Detection Using Web Images. - 1 online resource (27 pages)

Source: Masters Abstracts International, Volume: 84-01.

Thesis (M.S.)--The University of Mississippi, 2022.

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798834044734Subjects--Topical Terms:

561178
Information science.
Subjects--Index Terms:

Face detectionIndex Terms--Genre/Form:

554714
Electronic books.

Face Detection Using Web Images.
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502 $a Thesis (M.S.)--The University of Mississippi, 2022.
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520 $a 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.
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538 $a Mode of access: World Wide Web
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650 4 $a Computer science. $3 573171
653 $a Face detection
653 $a Web images
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