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Heterogeneous deep metric learning for cross-modal biometric recognition

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dc.contributor.author Garg, Rishabh
dc.contributor.author Baweja, Yashasvi
dc.contributor.author Vatsa, Mayank (Advisor)
dc.contributor.author Singh, Richa (Advisor)
dc.date.accessioned 2019-10-07T07:18:55Z
dc.date.available 2019-10-07T07:18:55Z
dc.date.issued 2018-11
dc.identifier.uri http://repository.iiitd.edu.in/xmlui/handle/123456789/750
dc.description.abstract Heterogeneous biometric recognition requires matching images with variations such as resolution and spectrum. Heterogeneity in images often reduces the inter-class homogeneous distance while increasing the intra-class heterogeneous distance. In this research, a novel metric learning method is proposed, which minimizes the intra-class homogeneous and heterogeneous distances while maximizing the inter-class homogeneous and heterogeneous distances. The effectiveness of the proposed algorithm is demonstrated on three face databases and three periocular databases corresponding to real-world heterogeneous biometric recognition problems. The experiments show that the proposed algorithm provides state-of-the-art results on all the databases and outperforms existing recognition and metric learning algorithms. Further another use case is presented for mobile periocular recognition, proving the efficacy of the proposed model for unconstrained settings. en_US
dc.language.iso en_US en_US
dc.publisher IIITD-Delhi en_US
dc.subject Distance Metric Learning en_US
dc.subject Periocular Recognition en_US
dc.subject Face Recognition en_US
dc.subject Heterogeneous en_US
dc.subject Cross-domain en_US
dc.title Heterogeneous deep metric learning for cross-modal biometric recognition en_US
dc.type Other en_US

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