A statistical quadtree decomposition to improve face analysis

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dc.contributor.authorAmaral V.
dc.contributor.authorGiraldi G. A.
dc.contributor.authorCarlos E. Thomaz
dc.contributor.authorOrcidhttps://orcid.org/0000-0001-5566-1963
dc.date.accessioned2022-01-12T21:59:29Z
dc.date.available2022-01-12T21:59:29Z
dc.date.issued2016-02-24
dc.description.abstract© Copyright 2016 by SCITEPRESS-Science and Technology Publications, Lda. All rights reserved.The feature extraction is one of the most important steps in face analysis applications and this subject always received attention in the computer vision and pattern recognition areas due to its applicability and wide scope. However, to define the correct spatial relevance of physiognomical features remains a great challenge. It has been proposed recently, with promising results, a statistical spatial mapping technique that highlights the most discriminating facial features using some task driven information from data mining. Such priori information has been employed as a spatial weighted map on Local Binary Pattern (LBP), that uses Chi-Square distance as a nearest neighbour based classifier. Intending to reduce the dimensionality of LBP descriptors and improve the classification rates we propose and implement in this paper two quad-tree image decomposition algorithms to task related spatial map segmentation. The first relies only on split step (top-down) of distinct regions and the second performs the split step followed by a merge step (bottom-up) to combine similar adjacent regions. We carried out the experiments with two distinct face databases and our preliminary results show that the top-down approach achieved similar classification results to standard segmentation using though less regions.
dc.description.firstpage375
dc.description.lastpage380
dc.identifier.citationAMARAL, V.; GIRALDI, G. A.; THOMAZ, C. E. ICPRAM 2016 Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods. A statistical quadtree decomposition to improve face analysis, p. 375-380, 2016.
dc.identifier.doi10.5220/0005823903750380
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/3934
dc.relation.ispartofICPRAM 2016 - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods
dc.rightsAcesso Restrito
dc.subject.otherlanguageFace recognition
dc.subject.otherlanguageLocal binary pattern
dc.subject.otherlanguageQuadtree
dc.subject.otherlanguageSpatial mapping
dc.subject.otherlanguageTask driven
dc.titleA statistical quadtree decomposition to improve face analysis
dc.typeArtigo de evento
fei.scopus.citations1
fei.scopus.eid2-s2.0-84969909677
fei.scopus.subjectChi Square distance
fei.scopus.subjectClassification rates
fei.scopus.subjectClassification results
fei.scopus.subjectLocal binary patterns
fei.scopus.subjectQuad trees
fei.scopus.subjectQuad-tree decomposition
fei.scopus.subjectSpatial mapping
fei.scopus.subjectTask-driven
fei.scopus.updated2024-12-01
fei.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84969909677&origin=inward
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