  The de facto algorithm for facial landmark estimation involves running a face
detector with a subsequent deformable model fitting on the bounding box. This
encompasses two basic problems: i) the detection and deformable fitting steps
are performed independently, while the detector might not provide best-suited
initialisation for the fitting step, ii) the face appearance varies hugely
across different poses, which makes the deformable face fitting very
challenging and thus distinct models have to be used (\eg, one for profile and
one for frontal faces). In this work, we propose the first, to the best of our
knowledge, joint multi-view convolutional network to handle large pose
variations across faces in-the-wild, and elegantly bridge face detection and
facial landmark localisation tasks. Existing joint face detection and landmark
localisation methods focus only on a very small set of landmarks. By contrast,
our method can detect and align a large number of landmarks for semi-frontal
(68 landmarks) and profile (39 landmarks) faces. We evaluate our model on a
plethora of datasets including standard static image datasets such as IBUG,
300W, COFW, and the latest Menpo Benchmark for both semi-frontal and profile
faces. Significant improvement over state-of-the-art methods on deformable face
tracking is witnessed on 300VW benchmark. We also demonstrate state-of-the-art
results for face detection on FDDB and MALF datasets.
