Facial feature point extraction using the adaptive mean shape in active shape model
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Hyun-Chul | - |
dc.contributor.author | Kim, Hyoung-Joon | - |
dc.contributor.author | Hwang, Wonjun | - |
dc.contributor.author | Kee, Seok-Cheol | - |
dc.contributor.author | Kim, Whoi Yul | - |
dc.date.accessioned | 2022-12-21T08:56:41Z | - |
dc.date.available | 2022-12-21T08:56:41Z | - |
dc.date.created | 2022-09-16 | - |
dc.date.issued | 2007-03 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/180353 | - |
dc.description.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 | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Springer Verlag | - |
dc.title | Facial feature point extraction using the adaptive mean shape in active shape model | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Whoi Yul | - |
dc.identifier.doi | 10.1007/978-3-540-71457-6_38 | - |
dc.identifier.scopusid | 2-s2.0-37149038081 | - |
dc.identifier.bibliographicCitation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.4418 LNCS, pp.421 - 429 | - |
dc.relation.isPartOf | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.title | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.volume | 4418 LNCS | - |
dc.citation.startPage | 421 | - |
dc.citation.endPage | 429 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Adaptive systems | - |
dc.subject.keywordPlus | Feature extraction | - |
dc.subject.keywordPlus | Problem solving | - |
dc.subject.keywordPlus | Statistical methods | - |
dc.subject.keywordPlus | Vectors | - |
dc.subject.keywordPlus | Active shape model | - |
dc.subject.keywordPlus | Facial feature extraction | - |
dc.subject.keywordPlus | Multipose faces | - |
dc.subject.keywordPlus | Face recognition | - |
dc.identifier.url | https://link.springer.com/chapter/10.1007/978-3-540-71457-6_38 | - |
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