Cascaded Regression (CR) based methods have been proposed to solve facial
landmarks detection problem, which learn a series of descent directions by
multiple cascaded regressors separately trained in coarse and fine stages. They
outperform the traditional gradient descent based methods in both accuracy and
running speed. However, cascaded regression is not robust enough because each
regressor's training data comes from the output of previous regressor.
Moreover, training multiple regressors requires lots of computing resources,
especially for deep learning based methods. In this paper, we develop a
Self-Iterative Regression (SIR) framework to improve the model efficiency. Only
one self-iterative regressor is trained to learn the descent directions for
samples from coarse stages to fine stages, and parameters are iteratively
updated by the same regressor. Specifically, we proposed Landmarks-Attention
Network (LAN) as our regressor, which concurrently learns features around each
landmark and obtains the holistic location increment. By doing so, not only the
rest of regressors are removed to simplify the training process, but the number
of model parameters is significantly decreased. The experiments demonstrate
that with only 3.72M model parameters, our proposed method achieves the
state-of-the-art performance.

本研究提出了自我迭代回归框架，通过 Landmarks-Attention 网络作为回归器，将级联回归方法中的多个回归器替换为一个自我迭代回归器，从而显着减少了模型参数，提高了模型效率。实验结果表明，该方法仅需 3.72M 模型参数即可达到最先进的性能水平。