dc.contributor.author | Padilha, Rafael | |
dc.contributor.author | Andaló, Fernanda | |
dc.contributor.author | Bertocco, Gabriel | |
dc.contributor.author | Almeida, Waldir | |
dc.contributor.author | Dias, William | |
dc.contributor.author | Resek, Thiago | |
dc.contributor.author | Torres, Ricardo Da Silva | |
dc.contributor.author | Wainer, Jacques | |
dc.contributor.author | Rocha, Anderson | |
dc.date.accessioned | 2021-09-06T11:22:40Z | |
dc.date.available | 2021-09-06T11:22:40Z | |
dc.date.created | 2020-09-12T18:02:21Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | IET Biometrics. 2020, 9 (5), 205-215. | en_US |
dc.identifier.issn | 2047-4938 | |
dc.identifier.uri | https://hdl.handle.net/11250/2773745 | |
dc.description.abstract | Mobile devices have their popularity and affordability greatly increased in recent years. As a consequence of their ubiquity, these devices now carry all sorts of personal data that should be accessed only by their owner. Even though knowledge-based procedures are still the main methods to secure the owner's identity, recently biometric traits have been employed for more secure and effortless authentication. In this work, the authors propose a facial verification method optimised to the mobile environment. It consists of a two-tiered procedure that combines hand-crafted features and a convolutional neural network (CNN) to verify if the person depicted in a photograph corresponds to the device owner. To train a CNN for the verification task, the authors propose a hybrid-image input, which allows the network to process encoded information of a pair of face images. The proposed experiments show that the solution outperforms state of the art face verification methods, providing a 4× speedup when processing an image in recent smartphone models. Additionally, the authors show that the two-tiered procedure can be coupled with existing face verification CNNs improving their accuracy and efficiency. They also present a new data set of selfie pictures – RECOD Selfie data set – that hopefully will support future research in this scenario. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Institution of Engineering and Technology (IET) | en_US |
dc.title | Two-tiered face verification with low-memory footprint for mobile devices | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.source.pagenumber | 205-215 | en_US |
dc.source.volume | 9 | en_US |
dc.source.journal | IET Biometrics | en_US |
dc.source.issue | 5 | en_US |
dc.identifier.doi | 10.1049/iet-bmt.2020.0031 | |
dc.identifier.cristin | 1829361 | |
dc.description.localcode | This article will not be available due to copyright restrictions (c) 2020 by Institution of Engineering and Technology (IET) | en_US |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |