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Facial feature point extraction using the adaptive mean shape in active shape model

Authors
Kim, Hyun-ChulKim, Hyoung-JoonHwang, WonjunKee, Seok-CheolKim, Whoi Yul
Issue Date
Mar-2007
Publisher
Springer Verlag
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.4418 LNCS, pp.421 - 429
Indexed
SCOPUS
Journal Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
4418 LNCS
Start Page
421
End Page
429
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/180353
DOI
10.1007/978-3-540-71457-6_38
ISSN
0302-9743
Abstract
The fixed mean shape that is built from the statistical shape model produces an erroneous feature extraction result when ASM is applied to multipose faces. To remedy this problem the mean shape vector which is similar to an input face image is needed. In this paper, we propose the adaptive mean shape to extract facial features accurately for non frontal face. It indicates the mean shape vector that is the most similar to the face form of the input image. Our experimental results show that the proposed method obtains feature point positions with high accuracy and significantly improving the performance of facial feature extraction over and above that of the original ASM
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서울 공과대학 > 서울 융합전자공학부 > 1. Journal Articles

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