# Facial Landmark Detection with Tweaked Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1511.04031 · HTML: https://mlsanity.com/paper/1511.04031/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2015.

## Built on by (most cited first, 13 of 13)

- [A Multiplexed Network for End-to-End, Multilingual OCR](https://mlsanity.com/paper/2103.15992/) (2021)
- [Deep Face Restoration: A Survey](https://mlsanity.com/paper/2211.02831/) (2022)
- [Deep transfer learning for image classification: a survey](https://mlsanity.com/paper/2205.09904/) (2022)
- [FaceShot: Bring Any Character into Life](https://mlsanity.com/paper/2503.00740/) (2025)
- [Learn Fine-grained Adaptive Loss for Multiple Anatomical Landmark Detection in Medical Images](https://mlsanity.com/paper/2105.09124/) (2021)
- [Generalizable Face Landmarking Guided by Conditional Face Warping](https://mlsanity.com/paper/2404.12322/) (2024)
- [OpenFace 3.0: A Lightweight Multitask System for Comprehensive Facial Behavior Analysis](https://mlsanity.com/paper/2506.02891/) (2025)
- [Distance Map Supervised Landmark Localization for MR-TRUS Registration](https://mlsanity.com/paper/2210.05738/) (2022)
- [Mitigating Knowledge Discrepancies among Multiple Datasets for Task-agnostic Unified Face Alignment](https://mlsanity.com/paper/2503.22359/) (2025)
- [Real-time Keypoints Detection for Autonomous Recovery of the Unmanned Ground Vehicle](https://mlsanity.com/paper/2107.12852/) (2021)
- [Facial Landmark Detection Evaluation on MOBIO Database](https://mlsanity.com/paper/2307.03329/) (2023)
- [Deformation Robust Text Spotting with Geometric Prior](https://mlsanity.com/paper/2308.16404/) (2023)
- [Rethinking Medical Landmark Localization with Prototype Learning-based Progressive Offset Correction](https://mlsanity.com/paper/2608.09182/) (2026)

## Abstract (arXiv)

We present a novel convolutional neural network (CNN) design for facial landmark coordinate regression. We examine the intermediate features of a standard CNN trained for landmark detection and show that features extracted from later, more specialized layers capture rough landmark locations. This provides a natural means of applying differential treatment midway through the network, tweaking processing based on facial alignment. The resulting Tweaked CNN model (TCNN) harnesses the robustness of CNNs for landmark detection, in an appearance-sensitive manner without training multi-part or multi-scale models. Our results on standard face landmark detection and face verification benchmarks show TCNN to surpasses previously published performances by wide margins.