The first Facial Landmark Tracking in-the-Wild Challenge: benchmark and results

Shen, Jie; Zafeiriou, Stefanos; Chrysos, Grigorios G.; Kossaifi, Jean; Tzimiropoulos, Georgios; Pantic, Maja

Abstract

Detection and tracking of faces in image sequences is among the most well studied problems in the intersection of statistical machine learning and computer vision. Often, tracking and detection methodologies use a rigid representation to describe the facial region 1, hence they can neither capture nor exploit the non-rigid facial deformations, which are crucial for countless of applications (e.g., facial expression analysis, facial motion capture, high-performance face recognition etc.). Usually, the non-rigid deformations are captured by locating and tracking the position of a set of fiducial facial landmarks (e.g., eyes, nose, mouth etc.). Recently, we witnessed a burst of research in automatic facial landmark localisation in static imagery. This is partly attributed to the availability of large amount of annotated data, many of which have been provided by the first facial landmark localisation challenge (also known as 300-W challenge). Even though now well established benchmarks exist for facial landmark localisation in static imagery, to the best of our knowledge, there is no established benchmark for assessing the performance of facial landmark tracking methodologies, containing an adequate number of annotated face videos. In conjunction with ICCV’2015 we run the first competition/challenge on facial landmark tracking in long-term videos. In this paper, we present the first benchmark for long-term facial landmark tracking, containing currently over 110 annotated videos, and we summarise the results of the competition.

Citation

Shen, J., Zafeiriou, S., Chrysos, G. G., Kossaifi, J., Tzimiropoulos, G., & Pantic, M. The first Facial Landmark Tracking in-the-Wild Challenge: benchmark and results. Presented at 2015 IEEE International Conference on Computer Vision

• Conference Name: 2015 IEEE International Conference on Computer Vision
• End Date: Dec 13, 2015
• Publication Date: Dec 1, 2015
• Deposit Date: Jan 29, 2016
• Publicly Available Date: Jan 29, 2016
• Peer Reviewed: Peer Reviewed
• Public URL: https://nottingham-repository.worktribe.com/output/981125
• Publisher URL: http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w25/html/Shen_The_First_Facial_ICCV_2015_paper.html