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Video-based facial expression analysis.
~
Li, Zhiguo.
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Video-based facial expression analysis.
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Video-based facial expression analysis./
作者:
Li, Zhiguo.
面頁冊數:
122 p.
附註:
Source: Dissertation Abstracts International, Volume: 72-03, Section: B, page: 1580.
Contained By:
Dissertation Abstracts International72-03B.
標題:
Computer Science. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3434722
ISBN:
9781124419725
Video-based facial expression analysis.
Li, Zhiguo.
Video-based facial expression analysis.
- 122 p.
Source: Dissertation Abstracts International, Volume: 72-03, Section: B, page: 1580.
Thesis (Ph.D.)--Rutgers The State University of New Jersey - New Brunswick, 2010.
Recognizing facial expressions from facial video sequences is an important and unsolved problem. Among many factors that contribute to the challenges of this task are: non-frontal facial poses, poorly aligned face images, large variations in the temporal scale of facial expressions, and the subtle differences between different subjects for the same facial expression etc. A successful video-based facial expression analysis system should be able to handle at least the following problems: robust face tracking, or spatial alignment of the faces, video segmentation, effective feature representation and selection schemes which are robust to face mis-alignment and temporal normalization by sequential classifier. In this work we report several advances we made in building various components of a system for classifying facial expressions from video inputs. Particularly, my work focus on robust face tracking, facial feature representation and selection under different face alignment conditions, sequential modeling for facial expression recognition. We performed extensive experiments using the proposed algorithms on publicly available dataset and achieved state of the art performances.
ISBN: 9781124419725Subjects--Topical Terms:
626642
Computer Science.
Video-based facial expression analysis.
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Recognizing facial expressions from facial video sequences is an important and unsolved problem. Among many factors that contribute to the challenges of this task are: non-frontal facial poses, poorly aligned face images, large variations in the temporal scale of facial expressions, and the subtle differences between different subjects for the same facial expression etc. A successful video-based facial expression analysis system should be able to handle at least the following problems: robust face tracking, or spatial alignment of the faces, video segmentation, effective feature representation and selection schemes which are robust to face mis-alignment and temporal normalization by sequential classifier. In this work we report several advances we made in building various components of a system for classifying facial expressions from video inputs. Particularly, my work focus on robust face tracking, facial feature representation and selection under different face alignment conditions, sequential modeling for facial expression recognition. We performed extensive experiments using the proposed algorithms on publicly available dataset and achieved state of the art performances.
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