Real-time hand pose estimation on a smart-phone using Deep Learning

Abstract: Hand pose estimation is a computer vision challenge that consists of detecting the coordinates of a hand’s key points in an image. This research investigates several deep learning-based solutions to determine whether or not it is possible to improve current state-of-the-art detectors for smartphone applications. Several models are tested and compared based on accuracy, processing speed and memory size. A final network is selected and detailed to compare it to the state-of-the-art. The proposed solution is obtained by combining the Differentiable Spatial to Numerical Transform layer to predict numerical coordinates together with the Fire module presented in the SqueezeNet architecture. This deep neural network contains around 1 million parameters and is able to outperform the current best documented model in all the metrics described above. A qualitative analysis is also performed to examine the predictions of the final solution on test images.