DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jalal, A. | - |
dc.contributor.author | Kamal, S. | - |
dc.contributor.author | KIM, DAI JIN | - |
dc.date.accessioned | 2019-12-03T11:50:37Z | - |
dc.date.available | 2019-12-03T11:50:37Z | - |
dc.date.created | 2018-07-18 | - |
dc.date.issued | 2017-07 | - |
dc.identifier.issn | 1975-0102 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/100183 | - |
dc.description.abstract | Facial expression recognition systems using video devices have emerged as an important component of natural human-machine interfaces which contribute to various practical applications such as security systems, behavioral science and clinical practices. In this work, we present a new method to analyze, represent and recognize human facial expressions using a sequence of facial images. Under our proposed facial expression recognition framework, the overall procedure includes: accurate face detection to remove background and noise effects from the raw image sequences and align each image using vertex mask generation. Furthermore, these features are reduced by principal component analysis. Finally, these augmented features are trained and tested using Hidden Markov Model (HMM). The experimental evaluation demonstrated the proposed approach over two public datasets such as Cohn-Kanade and AT&T datasets of facial expression videos that achieved expression recognition results as 96.75% and 96.92%. Besides, the recognition results show the superiority of the proposed approach over the state of the art methods. ? 2017, Korean Institute of Electrical Engineers. All rights reserved. | - |
dc.language | English | - |
dc.publisher | Korean Institute of Electrical Engineers | - |
dc.relation.isPartOf | Journal of Electrical Engineering and Technology | - |
dc.title | Facial expression recognition using 1D transform features and Hidden Markov Model | - |
dc.type | Article | - |
dc.identifier.doi | 10.5370/JEET.2017.12.4.1657 | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | Journal of Electrical Engineering and Technology, v.12, no.4, pp.1657 - 1662 | - |
dc.identifier.kciid | ART002231480 | - |
dc.identifier.wosid | 000404034200038 | - |
dc.citation.endPage | 1662 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 1657 | - |
dc.citation.title | Journal of Electrical Engineering and Technology | - |
dc.citation.volume | 12 | - |
dc.contributor.affiliatedAuthor | KIM, DAI JIN | - |
dc.identifier.scopusid | 2-s2.0-85020921118 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.type.docType | ARTICLE | - |
dc.subject.keywordPlus | Behavioral research | - |
dc.subject.keywordPlus | Hidden Markov models | - |
dc.subject.keywordPlus | Markov processes | - |
dc.subject.keywordPlus | Principal component analysis | - |
dc.subject.keywordPlus | D-transform | - |
dc.subject.keywordPlus | Experimental evaluation | - |
dc.subject.keywordPlus | Expression recognition | - |
dc.subject.keywordPlus | Facial expression recognition | - |
dc.subject.keywordPlus | Facial Expressions | - |
dc.subject.keywordPlus | Human facial expressions | - |
dc.subject.keywordPlus | Human Machine Interface | - |
dc.subject.keywordPlus | State-of-the-art methods | - |
dc.subject.keywordPlus | Face recognition | - |
dc.subject.keywordAuthor | 1D transform | - |
dc.subject.keywordAuthor | Facial expression | - |
dc.subject.keywordAuthor | Hidden Markov model | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
library@postech.ac.kr Tel: 054-279-2548
Copyrights © by 2017 Pohang University of Science ad Technology All right reserved.