  Accurate hand joints detection from images is a fundamental topic which is
essential for many applications in computer vision and human computer
interaction. This paper presents a two stage network for hand joints detection
from single unmarked image by using serial-parallel multi-scale feature fusion.
In stage I, the hand regions are located by a pre-trained network, and the
features of each detected hand region are extracted by a shallow spatial hand
features representation module. The extracted hand features are then fed into
stage II, which consists of serially connected feature extraction modules with
similar structures, called "multi-scale feature fusion" (MSFF). A MSFF contains
parallel multi-scale feature extraction branches, which generate initial hand
joint heatmaps. The initial heatmaps are then mutually reinforced by the
anatomic relationship between hand joints. The experimental results on five
hand joints datasets show that the proposed network overperforms the
state-of-the-art methods.
