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BioMetricNet: deep unconstrained face verification through learning of metrics regularized onto Gaussian distributions
We present BioMetricNet: a novel framework for deep unconstrained face verification which learns a regularized metric to compare facial features.
Learning mappings onto regularized latent spaces for biometric authentication
Differently from other methods, RegNet learns a mapping of the input biometric traits onto a target distribution in a well-behaved space in which users can be separated by means of simple and tunable boundaries.
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