  There is an urgent need to apply face alignment in a memory-efficient and
real-time manner due to the recent explosion of face recognition applications.
However, impact factors such as large pose variation and computational
inefficiency, still hinder its broad implementation. To this end, we propose a
computationally efficient deep evolutionary model integrated with 3D Diffusion
Heap Maps (DHM). First, we introduce a sparse 3D DHM to assist the initial
modeling process under extreme pose conditions. Afterward, a simple and
effective CNN feature is extracted and fed to Recurrent Neural Network (RNN)
for evolutionary learning. To accelerate the model, we propose an efficient
network structure to accelerate the evolutionary learning process through a
factorization strategy. Extensive experiments on three popular alignment
databases demonstrate the advantage of the proposed models over the
state-of-the-art, especially under large-pose conditions. Notably, the
computational speed of our model is 6 times faster than the state-of-the-art on
CPU and 14 times on GPU. We also discuss and analyze the limitations of our
models and future research work.
