# A Two-Step Learning Method For Detecting Landmarks on Faces From Different Domains

arXiv: https://arxiv.org/abs/1809.04621 · HTML: https://mlsanity.com/paper/1809.04621/

## Key facts

- Cited by 1 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 12 Sep 2018.

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

- [DogFLW: Dog Facial Landmarks in the Wild Dataset](https://mlsanity.com/paper/2405.11501/) (2024)

## Abstract (arXiv)

The detection of fiducial points on faces has significantly been favored by the rapid progress in the field of machine learning, in particular in the convolution networks. However, the accuracy of most of the detectors strongly depends on an enormous amount of annotated data. In this work, we present a domain adaptation approach based on a two-step learning to detect fiducial points on human and animal faces. We evaluate our method on three different datasets composed of different animal faces (cats, dogs, and horses). The experiments show that our method performs better than state of the art and can use few annotated data to leverage the detection of landmarks reducing the demand for large volume of annotated data.