# Facial Landmark Detection Evaluation on MOBIO Database

arXiv: https://arxiv.org/abs/2307.03329 · HTML: https://mlsanity.com/paper/2307.03329/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 55 references that are in the map.
- First posted on arXiv 6 Jul 2023.

## Builds on (most cited first, 16 of 16)

- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://mlsanity.com/paper/1704.08063/) (2017)
- [MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videos](https://mlsanity.com/paper/1606.06259/) (2016)
- [Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks](https://mlsanity.com/paper/1711.06753/) (2017)
- [End-to-end 3D face reconstruction with deep neural networks](https://mlsanity.com/paper/1704.05020/) (2017)
- [Neural Aggregation Network for Video Face Recognition](https://mlsanity.com/paper/1603.05474/) (2016)
- [Effective Face Frontalization in Unconstrained Images](https://mlsanity.com/paper/1411.7964/) (2014)
- [Learning Deep Representation for Face Alignment with Auxiliary Attributes](https://mlsanity.com/paper/1408.3967/) (2014)
- [Dynamic Attention-controlled Cascaded Shape Regression Exploiting Training Data Augmentation and Fuzzy-set Sample Weighting](https://mlsanity.com/paper/1611.05396/) (2016)
- [Facial Landmark Detection with Tweaked Convolutional Neural Networks](https://mlsanity.com/paper/1511.04031/) (2015)
- [Simultaneous Facial Landmark Detection, Pose and Deformation Estimation under Facial Occlusion](https://mlsanity.com/paper/1709.08130/) (2017)
- [Pose-Invariant 3D Face Alignment](https://mlsanity.com/paper/1506.03799/) (2015)
- [Combining Data-driven and Model-driven Methods for Robust Facial Landmark Detection](https://mlsanity.com/paper/1611.10152/) (2016)
- [Face Detection, Bounding Box Aggregation and Pose Estimation for Robust Facial Landmark Localisation in the Wild](https://mlsanity.com/paper/1705.02402/) (2017)
- [Constrained Joint Cascade Regression Framework for Simultaneous Facial Action Unit Recognition and Facial Landmark Detection](https://mlsanity.com/paper/1709.08129/) (2017)

## Abstract (arXiv)

MOBIO is a bi-modal database that was captured almost exclusively on mobile phones. It aims to improve research into deploying biometric techniques to mobile devices. Research has been shown that face and speaker recognition can be performed in a mobile environment. Facial landmark localization aims at finding the coordinates of a set of pre-defined key points for 2D face images. A facial landmark usually has specific semantic meaning, e.g. nose tip or eye centre, which provides rich geometric information for other face analysis tasks such as face recognition, emotion estimation and 3D face reconstruction. Pretty much facial landmark detection methods adopt still face databases, such as 300W, AFW, AFLW, or COFW, for evaluation, but seldomly use mobile data. Our work is first to perform facial landmark detection evaluation on the mobile still data, i.e., face images from MOBIO database. About 20,600 face images have been extracted from this audio-visual database and manually labeled with 22 landmarks as the groundtruth. Several state-of-the-art facial landmark detection methods are adopted to evaluate their performance on these data. The result shows that the data from MOBIO database is pretty challenging. This database can be a new challenging one for facial landmark detection evaluation.