9,817 research outputs found
Self-supervised learning of a facial attribute embedding from video
We propose a self-supervised framework for learning facial attributes by
simply watching videos of a human face speaking, laughing, and moving over
time. To perform this task, we introduce a network, Facial Attributes-Net
(FAb-Net), that is trained to embed multiple frames from the same video
face-track into a common low-dimensional space. With this approach, we make
three contributions: first, we show that the network can leverage information
from multiple source frames by predicting confidence/attention masks for each
frame; second, we demonstrate that using a curriculum learning regime improves
the learned embedding; finally, we demonstrate that the network learns a
meaningful face embedding that encodes information about head pose, facial
landmarks and facial expression, i.e. facial attributes, without having been
supervised with any labelled data. We are comparable or superior to
state-of-the-art self-supervised methods on these tasks and approach the
performance of supervised methods.Comment: To appear in BMVC 2018. Supplementary material can be found at
http://www.robots.ox.ac.uk/~vgg/research/unsup_learn_watch_faces/fabnet.htm
Head Pose Estimation via Manifold Learning
For the last decades, manifold learning has shown its advantage of efficient non-linear dimensionality reduction in data analysis. Based on the assumption that informative and discriminative representation of the data lies on a low-dimensional smooth manifold which implicitly embedded in the original high-dimensional space, manifold learning aims to learn the low-dimensional representation following some geometrical protocols, such as preserving piecewise local structure of the original data. Manifold learning also plays an important role in the applications of computer vision, i.e., face image analysis. According to the observations that many face-related research is benefitted by the head pose estimation, and the continuous variation of head pose can be modelled and interpreted as a low-dimensional smooth manifold, we will focus on the head pose estimation via manifold learning in this chapter. Generally, head pose is hard to directly explore from the high-dimensional space interpreted as face images, which is, however, can be efficiently represented in low-dimensional manifold. Therefore, in this chapter, classical manifold learning algorithms are introduced and the corresponding application on head pose estimation are elaborated. Several extensions of manifold learning algorithms which are developed especially for head pose estimation are also discussed and compared
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