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Facial key features detection algori...

Munteanu, Ivan.

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Facial key features detection algorithm implementation.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Facial key features detection algorithm implementation./
Author:	Munteanu, Ivan.
Description:	129 p.
Notes:	Source: Masters Abstracts International, Volume: 52-06.
Contained By:	Masters Abstracts International52-06(E).
Subject:	Information Technology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1554710
ISBN:	9781303858758

Facial key features detection algorithm implementation.
Munteanu, Ivan.

Facial key features detection algorithm implementation. - 129 p.

Source: Masters Abstracts International, Volume: 52-06.

Thesis (M.S.)--University of Nebraska at Omaha, 2014.

The goal of this thesis is to develop an algorithm for detecting facial key features on grayscale images. The task is complex and has the potential to be implemented in many fields such as medicine, security, automotive industry and gaming.

ISBN: 9781303858758Subjects--Topical Terms:

1030799
Information Technology.

Facial key features detection algorithm implementation.
LDR:01621nam a2200313 4500 001 1967266
005 20141112080247.5
008 150210s2014 ||||||||||||||||| ||eng d
020 $a 9781303858758
035 $a (MiAaPQ)AAI1554710
035 $a AAI1554710
040 $a MiAaPQ $c MiAaPQ
100 1 $a Munteanu, Ivan. $3 2104226
245 1 0 $a Facial key features detection algorithm implementation.
300 $a 129 p.
500 $a Source: Masters Abstracts International, Volume: 52-06.
500 $a Adviser: Lotfollah Najjar.
502 $a Thesis (M.S.)--University of Nebraska at Omaha, 2014.
520 $a The goal of this thesis is to develop an algorithm for detecting facial key features on grayscale images. The task is complex and has the potential to be implemented in many fields such as medicine, security, automotive industry and gaming.
520 $a The code was written in Octave. This language is optimal for prototyping. It has a simple syntax and powerful statistical libraries.
520 $a This algorithm models a neural network for key feature patch detection using a sliding window and a linear regression for identifying the rest of the features. Obtained results generate a root square mean error around 4.28 and are satisfactory compared to the best results that are under 2.00. The model could be refined using a deep convolution neural network.
590 $a School code: 1060.
650 4 $a Information Technology. $3 1030799
650 4 $a Artificial Intelligence. $3 769149
650 4 $a Computer Science. $3 626642
690 $a 0489
690 $a 0800
690 $a 0984
710 2 $a University of Nebraska at Omaha. $b Computer Science. $3 1020791
773 0 $t Masters Abstracts International $g 52-06(E).
790 $a 1060
791 $a M.S.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1554710