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dc.contributor.authorDas, Abhijit
dc.contributor.authorSengupta, Abira
dc.contributor.authorSaqib, Muhammad
dc.contributor.authorPal, Umapada
dc.contributor.authorBlumenstein, Michael
dc.date.accessioned2019-05-29T12:58:22Z
dc.date.available2019-05-29T12:58:22Z
dc.date.issued2018
dc.identifier.isbn978-1-5090-6014-6
dc.identifier.issn2161-4407
dc.identifier.doi10.1109/ijcnn.2018.8489070
dc.identifier.urihttp://hdl.handle.net/10072/384044
dc.description.abstractIn this work, we propose a more realistic and efficient face-based mobile authentication technique using CNNs. This paper discusses and explores an inevitable problem of using face images for mobile authentication, taken from varying distances with a front/selfie camera of the mobile phone. Incidentally, once an individual comes towards a certain distance from the camera, the face images get large and appear over-sized. Simultaneously sharp features of some portions of the face, such as forehead, cheek, and chin are changed completely. As a result, the face features change and the impact increases exponentially once the individual crosses a certain distance and gradually approaches towards the front camera. This work proposes a solution (achieving better accuracy and facial features, whereby face images were cropped and aligned around its close bounding box) to mitigate the aforementioned identified gap. The work investigated different frontier face detection and recognition techniques to justify the proposed solution. Among all the employed methods evaluated, CNNs worked best. For a quantitative comparison of the proposed method, manually cropped face images/annotations of the face images along with their close boundary were prepared. In turn, we have developed a database considering the above-mentioned scenario for 40 individuals, which will be publicly available for academic research purposes. The experimental results achieved indicate a successful implementation of the proposed method and the performance of the proposed technique is also found to be superior in comparison to the existing state-of-the-art.
dc.description.peerreviewedYes
dc.publisherIEEE
dc.relation.ispartofconferencename2018 International Joint Conference on Neural Networks (IJCNN)
dc.relation.ispartofconferencetitle2018 International Joint Conference on Neural Networks (IJCNN)
dc.relation.ispartofdatefrom2018-07-08
dc.relation.ispartofdateto2018-07-13
dc.relation.ispartoflocationRio de Janeiro, Brazil
dc.relation.ispartofpagefrom1
dc.relation.ispartofpageto8
dc.subject.fieldofresearchArtificial intelligence
dc.subject.fieldofresearchcode4602
dc.titleMore Realistic and Efficient Face-Based Mobile Authentication using CNNs
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.hasfulltextNo Full Text
gro.griffith.authorDas, Abhijit


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