Our experiments prove that human aesthetic judgements are usually biased.
To this end, we organise a competition that provides a new benchmark dataset that contains 2000 2D facial images of 135 subjects as well as their 3D ground truth face scans.
We present a new loss function, namely Wing loss, for robust facial landmark localisation with Convolutional Neural Networks (CNNs).
Ranked #1 on Face Alignment on 300W (NME_inter-pupil (%, Common) metric)
In this paper, we show how a 3D Morphable Model (i. e. a statistical model of the 3D shape of a class of objects such as faces) can be used to spatially transform input data as a module (a 3DMM-STN) within a convolutional neural network.
The framework has four stages: face detection, bounding box aggregation, pose estimation and landmark localisation.
We present a new Cascaded Shape Regression (CSR) architecture, namely Dynamic Attention-Controlled CSR (DAC-CSR), for robust facial landmark detection on unconstrained faces.
Ranked #15 on Face Alignment on AFLW-19
Face analysis techniques have become a crucial component of human-machine interaction in the fields of assistive and humanoid robotics.
We present a fully automatic approach to real-time 3D face reconstruction from monocular in-the-wild videos.
In this paper, we present the Surrey Face Model, a multi-resolution 3D Morphable Model that we make available to the public for non-commercial purposes.