  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.
