Learning Channel Inter-dependencies at Multiple Scales on Dense Networks for Face Recognition
This work addresses face recognition in unconstrained settings, but appears incremental as it builds on existing concepts like dense networks and multi-scale features.
The authors tackled unconstrained face recognition by proposing a new deep network structure that integrates multi-scale feature learning, dense connections, and flow weighting, showing capability in learning complex data distributions from face images with various qualities.
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 evaluated in unconstrained face matching, showing the capability of learning complex data distributions caused by face images with various qualities.