Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/153466
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dc.contributor.authorPranjal, Swarupen_US
dc.contributor.authorKong, Adams Wai Kinen_US
dc.date.accessioned2021-12-05T06:45:11Z-
dc.date.available2021-12-05T06:45:11Z-
dc.date.issued2019-
dc.identifier.citationPranjal, S. & Kong, A. W. K. (2019). Palmprint recognition using realistic animation aided data augmentation. 2019 IEEE 10th International Conference on Biometrics Theory, Applications and Systems (BTAS). https://dx.doi.org/10.1109/BTAS46853.2019.9186003en_US
dc.identifier.isbn9781728115221-
dc.identifier.urihttps://hdl.handle.net/10356/153466-
dc.description.abstractIn this paper, a palmprint augmentation algorithm based on 3D animation is proposed for enhancing contactless palmprint recognition performance. Contactless palmprint varies in position, orientation and musculoskeletal deformations. As the existing contactless databases are small, they contain only a few such variations of a palm. Popular data augmentation approaches, including translation, rotation and scaling, have been used to increase the dataset size and its diversity, but these methods do not simulate non-linear deformation of the hand. Some researchers have used 3D and computer graphic techniques to generate more data for training deep networks. These techniques are application-specific. The proposed algorithm makes use of a 3D hand model to simulate muscular and skeletal deformations of the hand. The deformations from the 3D model are applied to 2D palmprint images to generate new palmprint images with the same identities. Four deep networks, Alexnet, VGG-16, Resnet-50 and Inception-V3 and two contactless palmprint databases, IITD and CASIA, are employed to evaluate the proposed algorithm. The proposed algorithm is compared with the standard augmentation methods. The experimental results show that the proposed augmentation algorithm reduces EER and Rank-1 error rate.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.language.isoenen_US
dc.relationMOE2016-T2-1-042(S)en_US
dc.rights© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/BTAS46853.2019.9186003.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titlePalmprint recognition using realistic animation aided data augmentationen_US
dc.typeConference Paperen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.contributor.conference2019 IEEE 10th International Conference on Biometrics Theory, Applications and Systems (BTAS)en_US
dc.identifier.doi10.1109/BTAS46853.2019.9186003-
dc.description.versionAccepted versionen_US
dc.subject.keywordsBiometricsen_US
dc.subject.keywordsDeep Learningen_US
dc.citation.conferencelocationTampa, Florida, USAen_US
dc.description.acknowledgementThis work is partially supported by the Ministry of Education, Singapore through Academic Research Fund Tier 2, MOE2016-T2-1-042(S).en_US
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