  Tracking Facial Points in unconstrained videos is challenging due to the
non-rigid deformation that changes over time. In this paper, we propose to
exploit incremental learning for person-specific alignment in wild conditions.
Our approach takes advantage of part-based representation and cascade
regression for robust and efficient alignment on each frame. Unlike existing
methods that usually rely on models trained offline, we incrementally update
the representation subspace and the cascade of regressors in a unified
framework to achieve personalized modeling on the fly. To alleviate the
drifting issue, the fitting results are evaluated using a deep neural network,
where well-aligned faces are picked out to incrementally update the
representation and fitting models. Both image and video datasets are employed
to valid the proposed method. The results demonstrate the superior performance
of our approach compared with existing approaches in terms of fitting accuracy
and efficiency.
