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Authenticating corrupted facial images on stand-alone DSP system

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dc.contributor.authorLee, SW-
dc.contributor.authorJung, HC-
dc.contributor.authorLee, SW-
dc.date.accessioned2021-09-09T06:58:51Z-
dc.date.available2021-09-09T06:58:51Z-
dc.date.created2021-06-19-
dc.date.issued2005-
dc.identifier.issn0302-9743-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/123276-
dc.description.abstractIn this paper, we propose a method of authenticating corrupted photo images based on noise parameter estimation and implement an authentication system using TMS320C6711 DSP chip. The proposed method first generates corrupted images and the noise parameters in the training phase. With a corrupted image and an original image, the noise parameters of the corrupted photo image can be estimated in the testing phase. Finally, we can make a synthesized photo image from the original photo image using the estimated noise parameters. We made some experiments on the prototype of the stand-alone system to verify the performance of the proposed method and to apply for real-life applications. The experimental results on this system show that the proposed method can estimate the noise parameters accurately and improve the performance of photo image authentication.-
dc.languageEnglish-
dc.language.isoen-
dc.publisherSPRINGER-VERLAG BERLIN-
dc.titleAuthenticating corrupted facial images on stand-alone DSP system-
dc.typeArticle-
dc.contributor.affiliatedAuthorLee, SW-
dc.identifier.wosid000231117100103-
dc.identifier.bibliographicCitationAUDIO AND VIDEO BASED BIOMETRIC PERSON AUTHENTICATION, PROCEEDINGS, v.3546, pp.987 - 996-
dc.relation.isPartOfAUDIO AND VIDEO BASED BIOMETRIC PERSON AUTHENTICATION, PROCEEDINGS-
dc.citation.titleAUDIO AND VIDEO BASED BIOMETRIC PERSON AUTHENTICATION, PROCEEDINGS-
dc.citation.volume3546-
dc.citation.startPage987-
dc.citation.endPage996-
dc.type.rimsART-
dc.type.docTypeArticle; Proceedings Paper-
dc.description.journalClass1-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.relation.journalWebOfScienceCategoryComputer Science, Theory & Methods-
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