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Multi-scale facial scanning via spatial LSTM for latent facial feature representation

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dc.contributor.authorKim,Seong Tae
dc.contributor.authorChoi,Yeoreum
dc.contributor.authorRo,Yong Man
dc.contributor.editorBrömme,Arslan
dc.contributor.editorBusch,Christoph
dc.contributor.editorDantcheva,Antitza
dc.contributor.editorRathgeb,Christian
dc.contributor.editorUhl,Andreas
dc.date.accessioned2017-09-26T09:20:59Z
dc.date.available2017-09-26T09:20:59Z
dc.date.issued2017
dc.description.abstractIn the past few decades, automatic face recognition has been an important vision task. In this paper, we exploit the spatial relationships of facial local regions by using a novel deep network. In the proposed method, face is spatially scanned with spatial long short-term memory (LSTM) to encode the spatial correlation of facial regions. Moreover, with facial regions of various scales, the complementary information of the multi-scale facial features is encoded. Experimental results on public database showed that the proposed method outperformed the conventional methods by improving the face recognition accuracy under illumination variation.en
dc.identifier.isbn978-3-88579-664-0
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/4642
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofBIOSIG 2017
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-70
dc.subjectFace recognition
dc.subjectfacial feature representation
dc.subjectspatial LSTM
dc.subjectdeep learning
dc.titleMulti-scale facial scanning via spatial LSTM for latent facial feature representationen
gi.citation.endPage135
gi.citation.startPage127
gi.conference.date20.-22. September 2017
gi.conference.locationDarmstadt, Germany
gi.conference.sessiontitleRegular Research Papers

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P270 - BIOSIG 2017 - Proceedings of the 16th International Conference of the Biometrics Special Interest Group