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

Real-time Memory Efficient Large-pose Face Alignment via Deep Evolutionary Network