Facial Landmark Detection Evaluation on MOBIO Database

TL;DR

This paper evaluates the performance of state-of-the-art facial landmark detection methods on the MOBIO database, a challenging mobile face dataset with manually labeled landmarks, highlighting its potential as a new benchmark for mobile face analysis.

Contribution

First to evaluate facial landmark detection on the MOBIO mobile face dataset, providing insights into its challenges and establishing it as a new benchmark.

Findings

MOBIO faces are challenging for current methods

Approximately 20,600 images labeled with 22 landmarks

Performance varies significantly across methods

Abstract

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.