# Learning Channel Inter-dependencies at Multiple Scales on Dense Networks for Face Recognition

arXiv: https://arxiv.org/abs/1711.10103 · HTML: https://mlsanity.com/paper/1711.10103/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2017.

## Built on by (most cited first, 1 of 1)

- [Weight and Height Estimation from a Single Human Image Captured in the Wild](https://mlsanity.com/paper/2607.26104/) (2026)

## Abstract (arXiv)

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.