Real-time Memory Efficient Large-pose Face Alignment via Deep Evolutionary Network
This addresses the need for efficient face alignment in applications like face recognition, though it is incremental as it builds on existing deep learning and evolutionary approaches.
The paper tackles real-time, memory-efficient face alignment under large pose variations by proposing a deep evolutionary model with 3D Diffusion Heap Maps, achieving computational speeds 6 times faster on CPU and 14 times faster on GPU compared to state-of-the-art methods.
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.