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Learning Channel Inter-dependencies at Multiple Scales on Dense Networks for Face Recognition
[article]
2019
arXiv
pre-print
We propose a new deep network structure for unconstrained face recognition. The proposed network integrates several key components together in order to characterize complex data distributions, such as in unconstrained face images. Inspired by recent progress in deep networks, we consider some important concepts, including multi-scale feature learning, dense connections of network layers, and weighting different network flows, for building our deep network structure. The developed network is
arXiv:1711.10103v2
fatcat:vejhk3xsubbnlh5hmte37m3tcq