Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4394
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dc.contributor.authorDasgupta, Anirban-
dc.contributor.authorSengupta, Anwesha-
dc.contributor.authorBhattacharya, Shubhobrata-
dc.date.accessioned2024-02-15T11:48:09Z-
dc.date.available2024-02-15T11:48:09Z-
dc.date.issued2023-12-
dc.identifier.citation3rd International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET- 2023), NIT Patna, 21-22 December 2023en_US
dc.identifier.urihttp://hdl.handle.net/2080/4394-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractHeterogeneous Face Recognition (HFR) is gradually gaining importance in various domains including biometrics and cognitive studies. Classical methods are being replaced by deep learning techniques as a means to handle varied face modalities. The paper highlights the importance of HFR and introduces a face database that incorporates diversities in spectra, illumination, and formats (viz. photograph-sketch, longitudinal, 2D-3D). The role of deep learning methods has been discussed, and its advantages over the limitations of traditional methods have been emphasized. A comprehensive real-world HFR database, such as the one presented in the paper, will likely aid algorithm development, enriched by a neural networkbased curation technique that enhances diversity by excluding similar instances. The paper underscores the role of deep learning techniques in tackling the challenges in the field of HFR and presents the database as a significant contribution to advances in HFR research and application.en_US
dc.subjectHeterogeneous face recognitionen_US
dc.subjectface databaseen_US
dc.subjectNIRVIS face recognitionen_US
dc.subjectSketch-VIS face recognitionen_US
dc.titleIntegrating Heterogeneous Modalities for Comprehensive Facial Analysis: the Heteroface Databaseen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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