# A Comprehensive Performance Evaluation of Deformable Face Tracking "In-the-Wild"

arXiv: https://arxiv.org/abs/1603.06015 · HTML: https://mlsanity.com/paper/1603.06015/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2016.

## Built on by (most cited first, 5 of 5)

- [Supervision by Registration and Triangulation for Landmark Detection](https://mlsanity.com/paper/2101.09866/) (2021)
- [Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry](https://mlsanity.com/paper/2110.09772/) (2021)
- [Leveraging Deepfakes to Close the Domain Gap between Real and Synthetic Images in Facial Capture Pipelines](https://mlsanity.com/paper/2204.10746/) (2022)
- [Democratizing the Creation of Animatable Facial Avatars](https://mlsanity.com/paper/2401.16534/) (2024)
- [1DFormer: a Transformer Architecture Learning 1D Landmark Representations for Facial Landmark Tracking](https://mlsanity.com/paper/2311.00241/) (2023)

## Abstract (arXiv)

Recently, technologies such as face detection, facial landmark localisation and face recognition and verification have matured enough to provide effective and efficient solutions for imagery captured under arbitrary conditions (referred to as "in-the-wild"). This is partially attributed to the fact that comprehensive "in-the-wild" benchmarks have been developed for face detection, landmark localisation and recognition/verification. A very important technology that has not been thoroughly evaluated yet is deformable face tracking "in-the-wild". Until now, the performance has mainly been assessed qualitatively by visually assessing the result of a deformable face tracking technology on short videos. In this paper, we perform the first, to the best of our knowledge, thorough evaluation of state-of-the-art deformable face tracking pipelines using the recently introduced 300VW benchmark. We evaluate many different architectures focusing mainly on the task of on-line deformable face tracking. In particular, we compare the following general strategies: (a) generic face detection plus generic facial landmark localisation, (b) generic model free tracking plus generic facial landmark localisation, as well as (c) hybrid approaches using state-of-the-art face detection, model free tracking and facial landmark localisation technologies. Our evaluation reveals future avenues for further research on the topic.