# Facial Keypoints Detection

arXiv: https://arxiv.org/abs/1710.05279 · HTML: https://mlsanity.com/paper/1710.05279/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2017.

## Built on by (most cited first, 4 of 4)

- [Nonlinear optical encoding enabled by recurrent linear scattering](https://mlsanity.com/paper/2307.08558/) (2023)
- [Revisiting Facial Key Point Detection: An Efficient Approach Using Deep Neural Networks](https://mlsanity.com/paper/2205.07121/) (2022)
- [Chasing the Public Score: User Pressure and Evaluation Exploitation in Coding Agent Workflows](https://mlsanity.com/paper/2604.20200/) (2026)
- [Geometry-Aware Hierarchical Bayesian Learning on Manifolds](https://mlsanity.com/paper/2111.00184/) (2021)

## Abstract (arXiv)

Detect facial keypoints is a critical element in face recognition. However, there is difficulty to catch keypoints on the face due to complex influences from original images, and there is no guidance to suitable algorithms. In this paper, we study different algorithms that can be applied to locate keyponits. Specifically: our framework (1)prepare the data for further investigation (2)Using PCA and LBP to process the data (3) Apply different algorithms to analysis data, including linear regression models, tree based model, neural network and convolutional neural network, etc. Finally we will give our conclusion and further research topic. A comprehensive set of experiments on dataset demonstrates the effectiveness of our framework.