Heatmap regression has been used for landmark localization for quite a while
now. Most of the methods use a very deep stack of bottleneck modules for
heatmap classification stage, followed by heatmap regression to extract the
keypoints. In this paper, we present a single dendritic CNN, termed as Pose
Conditioned Dendritic Convolution Neural Network (PCD-CNN), where a
classification network is followed by a second and modular classification
network, trained in an end to end fashion to obtain accurate landmark points.
Following a Bayesian formulation, we disentangle the 3D pose of a face image
explicitly by conditioning the landmark estimation on pose, making it different
from multi-tasking approaches. Extensive experimentation shows that
conditioning on pose reduces the localization error by making it agnostic to
face pose. The proposed model can be extended to yield variable number of
landmark points and hence broadening its applicability to other datasets.
Instead of increasing depth or width of the network, we train the CNN
efficiently with Mask-Softmax Loss and hard sample mining to achieve upto
$15\%$ reduction in error compared to state-of-the-art methods for extreme and
medium pose face images from challenging datasets including AFLW, AFW, COFW and
IBUG.

本文提出了一种名为 PCD-CNN 的单一树突 CNN，使用分类网络和模块化分类网络训练，并通过贝叶斯公式显式解缠图像姿态与面部肢解点。经过广泛实验表明，本论文的方法能够通过姿态编码减少错误率，优于目前极具挑战性数据集的最先进方法（如 AFLW，AFW，COFW 和 IBUG）15％的误差。