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Facial Attribute Guided Deep Cross-Modal Hashing for Face Image Retrieval

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dc.contributor.authorTaherkhani, Fariborz
dc.contributor.authorTalreja, Veeru
dc.contributor.authorKazemi, Hadi
dc.contributor.authorNasrabadi, Nasser
dc.contributor.editorBrömme, Arslan
dc.contributor.editorBusch, Christoph
dc.contributor.editorDantcheva, Antitza
dc.contributor.editorRathgeb, Christian
dc.contributor.editorUhl, Andreas
dc.date.accessioned2019-06-17T10:00:13Z
dc.date.available2019-06-17T10:00:13Z
dc.date.issued2018
dc.description.abstractHashing-based image retrieval approaches have attracted much attention due to their fast query speed and low storage cost. In this paper, we propose an Attribute-based Deep Cross Modal Hashing (ADCMH) network which takes facial attribute modality as a query to retrieve relevant face images. The ADCMH network can efficiently generate compact binary codes to preserve similarity between two modalities (i.e., facial attribute and image modalities) in the Hamming space. Our ADCMH is an end to end deep cross-modal hashing network, which jointly learns similarity preserving features and also compensates for the quantization error due to the hashing of the continuous representation of modalities to binary codes. Experimental results on two standard datasets with facial attributes-image modalities indicate that our ADCMH face image retrieval model outperforms most of the current attribute-guided face image retrieval approaches, which are based on hand crafted features.en
dc.identifier.isbn978-3-88579-676-4
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/23782
dc.language.isoen
dc.publisherKöllen Druck+Verlag GmbH
dc.relation.ispartofBIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-283
dc.subjectFacial Attributes
dc.subjectFace Image Retrieval
dc.subjectDeep Hashing Network.
dc.titleFacial Attribute Guided Deep Cross-Modal Hashing for Face Image Retrievalen
dc.typeText/Conference Paper
gi.citation.publisherPlaceBonn
gi.conference.date26.-28. September 2018
gi.conference.locationDarmstadt

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P282 - BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group