DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yoon, J. | - |
dc.contributor.author | Kim, D. | - |
dc.date.accessioned | 2020-02-26T12:50:14Z | - |
dc.date.available | 2020-02-26T12:50:14Z | - |
dc.date.created | 2019-12-11 | - |
dc.date.issued | 2019-12 | - |
dc.identifier.issn | 1861-8200 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/101186 | - |
dc.description.abstract | We propose a novel multi-view face detector that operates accurately and fast in challenging environments. It consists of four consecutive functional components: background rejector, pose classifier, pose-specific face detectors, and face validator. The background rejector removes non-face patches quickly, the pose classifier estimates poses of the surviving patches, one or more selected pose-specific face detectors according to their estimated pose labels determine that a given patch is a face by using winner take all (WTA) strategy, and the face validator checks whether the face-like patch is really a face. For achieving strong discrimination power with low computing overhead, we devise several types of order relation features (ORF) that encode the order relation among feature elements as a unique code. The devised ORFs are placed in functional components appropriately to ensure fast operation of the multi-view face detector. For accurate classification, we propose a doubly domain-partitioning (DDP) classifier that consists of a coarse domain-partitioning weak classifier followed by a fine bin-partitioning weighted linear discriminant analysis (wLDA) classifier. For fast classification, we devise a feature sharing method that shares identical features between the background rejector and the pose classifier, and among all classes in the pose classifier. We evaluated the proposed multi-view face detector using the FDDB, AFW, and PASCAL face datasets. The experimental results show that the proposed multi-view face detector outperforms other state-of-the-art methods in terms of detection accuracy and execution time. | - |
dc.language | English | - |
dc.publisher | SPRINGER HEIDELBERG | - |
dc.relation.isPartOf | JOURNAL OF REAL-TIME IMAGE PROCESSING | - |
dc.title | An accurate and real-time multi-view face detector using ORFs and doubly domain-partitioning classifier | - |
dc.type | Article | - |
dc.identifier.doi | 10.1007/s11554-018-0751-6 | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | JOURNAL OF REAL-TIME IMAGE PROCESSING, v.16, no.6, pp.2425 - 2440 | - |
dc.identifier.wosid | 000501450300035 | - |
dc.citation.endPage | 2440 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 2425 | - |
dc.citation.title | JOURNAL OF REAL-TIME IMAGE PROCESSING | - |
dc.citation.volume | 16 | - |
dc.contributor.affiliatedAuthor | Yoon, J. | - |
dc.contributor.affiliatedAuthor | Kim, D. | - |
dc.identifier.scopusid | 2-s2.0-85041847818 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Background rejector | - |
dc.subject.keywordAuthor | Doubly domain-partitioning classifier | - |
dc.subject.keywordAuthor | Face validator | - |
dc.subject.keywordAuthor | Multi-view face detector | - |
dc.subject.keywordAuthor | Order relation feature | - |
dc.subject.keywordAuthor | Pose classifier | - |
dc.subject.keywordAuthor | Pose-specific face detector | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Imaging Science & Photographic Technology | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Imaging Science & Photographic Technology | - |
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