Face alignment is the task of identifying the geometric structure of faces in digital images, and attempting to obtain a canonical alignment of the face based on translation, scale, and rotation.
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We propose a straightforward method that simultaneously reconstructs the 3D facial structure and provides dense alignment.
SOTA for Face Alignment on AFLW-LFPA
In this paper, we propose to tackle these three challenges in an new alignment framework termed 3D Dense Face Alignment (3DDFA), in which a dense 3D Morphable Model (3DMM) is fitted to the image via Cascaded Convolutional Neural Networks.
Estimating the head pose of a person is a crucial problem that has a large amount of applications such as aiding in gaze estimation, modeling attention, fitting 3D models to video and performing face alignment.
SOTA for Head Pose Estimation on AFLW2000
Our method uses entire face images at all stages, contrary to the recently proposed face alignment methods that rely on local patches.
SOTA for Face Alignment on 300W
Instead, we compare our FPN with existing methods by evaluating how they affect face recognition accuracy on the IJB-A and IJB-B benchmarks: using the same recognition pipeline, but varying the face alignment method.
SOTA for Face Identification on IJB-B
Face detection and alignment in unconstrained environment are challenging due to various poses, illuminations and occlusions.
#9 best model for Face Detection on WIDER Face (Easy)
As a classic statistical model of 3D facial shape and texture, 3D Morphable Model (3DMM) is widely used in facial analysis, e. g., model fitting, image synthesis.
SOTA for Face Alignment on AFLW2000
Finally, to reduce the memory consumption and high precision operations both in training and testing, we further quantize weights, inputs, and gradients of our localization network to low bit-width numbers.
#4 best model for Pose Estimation on MPII Human Pose
We present a new loss function, namely Wing loss, for robust facial landmark localisation with Convolutional Neural Networks (CNNs).