  In this paper, we present a multi-scale Fully Convolutional Networks
(MSP-RFCN) to robustly detect and classify human hands under various
challenging conditions. In our approach, the input image is passed through the
proposed network to generate score maps, based on multi-scale predictions. The
network has been specifically designed to deal with small objects. It uses an
architecture based on region proposals generated at multiple scales. Our method
is evaluated on challenging hand datasets, namely the Vision for Intelligent
Vehicles and Applications (VIVA) Challenge and the Oxford hand dataset. It is
compared against recent hand detection algorithms. The experimental results
demonstrate that our proposed method achieves state-of-the-art detection for
hands of various sizes.
