Accurate fingertip detection from binocular mask images
Accurate fingertip detection is important for hand-based human computer interaction. Different from prior methods based on depth image, in this paper we propose a novel scheme for accurate fingertip detection from binocular mask images without explicitly computing the depth map. To demonstrate our proposed scheme, we build a new hand dataset containing synthetic and real binocular images. A deep convolutional neural network (CNN) is utilized as a baseline method to demonstrate the proposed scheme. The mask images are extracted from binocular images and fed into the CNN to predict the 3D positions of fingertips and palm center. Experiments show that this method achieves the mean error of 8.30mm on synthetic data and 4.64mm on real data, which is promosing for accurate 3D interaction. The proposed scheme runs at 130fps on a CPU and is promising for real-time applications.