# Disguised Face Identification (DFI) with Facial KeyPoints using Spatial Fusion Convolutional Network

arXiv: https://arxiv.org/abs/1708.09317 · HTML: https://mlsanity.com/paper/1708.09317/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2017.

## Built on by (most cited first, 2 of 2)

- [TAA-GCN: A Temporally Aware Adaptive Graph Convolutional Network for Age Estimation](https://mlsanity.com/paper/2305.08779/) (2023)
- [Visual Privacy: Current and Emerging Regulations Around Unconsented Video Analytics in Retail](https://mlsanity.com/paper/2302.12935/) (2023)

## Abstract (arXiv)

Disguised face identification (DFI) is an extremely challenging problem due to the numerous variations that can be introduced using different disguises. This paper introduces a deep learning framework to first detect 14 facial key-points which are then utilized to perform disguised face identification. Since the training of deep learning architectures relies on large annotated datasets, two annotated facial key-points datasets are introduced. The effectiveness of the facial keypoint detection framework is presented for each keypoint. The superiority of the key-point detection framework is also demonstrated by a comparison with other deep networks. The effectiveness of classification performance is also demonstrated by comparison with the state-of-the-art face disguise classification methods.