  Facial landmark detection is an essential technology for driver status
tracking and has been in demand for real-time estimations. As a landmark
coordinate prediction, heatmap-based methods are known to achieve a high
accuracy, and Lite-HRNet can achieve a fast estimation. However, with
Lite-HRNet, the problem of a heavy computational cost of the fusion block,
which connects feature maps with different resolutions, has yet to be solved.
In addition, the strong output module used in HRNetV2 is not applied to
Lite-HRNet. Given these problems, we propose a novel architecture called
Lite-HRNet Plus. Lite-HRNet Plus achieves two improvements: a novel fusion
block based on a channel attention and a novel output module with less
computational intensity using multi-resolution feature maps. Through
experiments conducted on two facial landmark datasets, we confirmed that
Lite-HRNet Plus further improved the accuracy in comparison with conventional
methods, and achieved a state-of-the-art accuracy with a computational
complexity with the range of 10M FLOPs.
