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Deep Learning based Kinship Verification on KinFaceW-I Dataset
Published in IEEE
Volume: 2019-October
Pages: 2529 - 2532
Face has been a prominent biometric trait and is widely appropriated for several tasks. One such a task is 'kinship verification' which primarily focuses on examining whether a disposed image pair appertains to a same family or not Kinship verification system grabs a large apportion of applications which are advantageous to the society. A convolutional neural network specific algorithm which can function as classifier between 'kin' and 'non-kin' categories has been proposed in this work. The proposed approach incorporates a novel deep learning based layered neural network architecture. This approach is evident to enact superior performance than a few reported algorithms and the classification entirely depends upon CNN specific feature vectors. We have applied the proposed technique on the Kinship Face in Wild (KinFaceW) dataset version - I which can be publicly obtained. Under a standard experimentation protocol, we have achieved an average verification accuracy of 86.94%. © 2019 IEEE.
About the journal
JournalData powered by TypesetTENCON 2019 - 2019 IEEE Region 10 Conference (TENCON)
PublisherData powered by TypesetIEEE
Open AccessNo